{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will help compare AOD estimates from GEOS-Chem with satellites and AERONET observations. This code was used  for the manuscript “Health impacts of smoke exposure in South America: Increased risk for populations in the Amazonian Indigenous territories.” (Bonilla et al., 2023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Latex error\n",
    "from matplotlib import rc\n",
    "rc(\"text\", usetex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "import cartopy\n",
    "from cartopy.io.shapereader import Reader\n",
    "from cartopy import config\n",
    "import cartopy.mpl.ticker as cticker\n",
    "from cartopy.feature import ShapelyFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import csv\n",
    "import glob\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as matplotlib\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "import matplotlib.dates as dplt\n",
    "from matplotlib.dates import date2num\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import pathlib\n",
    "\n",
    "# GESO-chem & GFED\n",
    "# those modules are almost always imported when working with model data\n",
    "%matplotlib inline\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import xarray as xr # the major tool to work with NetCDF data!\n",
    "import matplotlib.colors as colors\n",
    "from netCDF4 import Dataset\n",
    "#import h5py\n",
    "\n",
    "\n",
    "# matplotlibdate format modules\n",
    "from matplotlib.dates import DateFormatter\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns; \n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "import format_plots as fp\n",
    "\n",
    "Reds_trans = fp.cmap_trans('Reds')# makes tranparent at lower end\n",
    "Reds_trans_r = fp.cmap_trans('Reds')# makes tranparent in the middle of the spectrum\n",
    "Oranges_trans = fp.cmap_trans('Oranges')# makes tranparent at lower end\n",
    "Oranges_trans_r = fp.cmap_trans('Oranges')# makes tranparent in the middle of the spectrum\n",
    "# those modules are almost always imported when working with model data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##trying to open up AERONET data with python\n",
    "#AOD_AER= df = pd.read_table(\"/n/holyscratch01/mickley_lab/Users/ebonilla/All_Sites_Times_All_Points_AOD20.dat\")\n",
    "#AOD_AER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df=pd.read_csv(\"/n/holyscratch01/mickley_lab/Users/ebonilla/aeronet_locations_v3.txt\", sep=',',skiprows=0+1)\n",
    "#[-85, -30, -56, 13]\n",
    "#df_lon=df[(df[\"Longitude(decimal_degrees)\"]<-30) & (df[\"Longitude(decimal_degrees)\"]>-85)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_SA=df_lon[(df_lon[\"Latitude(decimal_degrees)\"]<13) & (df_lon[\"Latitude(decimal_degrees)\"]>-56)]\n",
    "#df_SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(df_SA['Site_Name'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "#### season_of_date\n",
    "* Classifies eaach date into a season based on Amazonian season.\n",
    "#### edit_Aeronet\n",
    "* edits the AERONET files to add columns with location information \n",
    "* Added columns: lat, lon, site name country, Andes? ,and datetime\n",
    "#### aeronet_timeseries\n",
    "* Deletes all unnecesary AERONET columns\n",
    "#### mean_cdfs\n",
    "* this function concatenates all the files and returns the following concactanated dataframes:\n",
    "* monthly, weekly, daily\n",
    "#### AERONET_seasonal_means\n",
    "* this function creates AERONET seasonal means\n",
    "#### GC_variable_means\n",
    "* this function creates means for Amazonial seasons, (Dry/Wet) and monthly \n",
    "#### Aerosols_2_AOD\n",
    "* this function adds up all the GEOS-Chem AOD components for corresponding wavelengths\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function adds the season in which the dates belong to \n",
    "def season_of_date(date):\n",
    "    year = str(date.year)\n",
    "    seasons = {'wet': pd.date_range(start='15/01/'+year, end='15/05/'+year),\n",
    "               'wet_dry': pd.date_range(start='16/05/'+year, end='15/07/'+year),\n",
    "               'dry': pd.date_range(start='15/07/'+year, end='15/11/'+year)}\n",
    "    if date in seasons['wet']:\n",
    "        return 'wet'\n",
    "    if date in seasons['wet_dry']:\n",
    "        return 'wet_dry'\n",
    "    if date in seasons['dry']:\n",
    "        return 'dry'\n",
    "    else:\n",
    "        return 'dry_wet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fucntions edits the AERONET file to add lat, lon, elevation, site name, country, location in Andes, and datetime \n",
    "#returns a df with the added columns\n",
    "def edit_Aeronet(df):#, filename):\n",
    "    #initalizing parameters\n",
    "    in_Andes='Y'\n",
    "    not_Andes='N'\n",
    "    #colummn index\n",
    "    idx=0\n",
    "    new_col = df['Date(dd:mm:yyyy)'] + \" \" +df['Time(hh:mm:ss)']\n",
    "    df.insert(loc=idx, column='datetime', value=new_col)\n",
    "    df['datetime']= pd.to_datetime(df['datetime'], format='%d:%m:%Y %H:%M:%S', utc=True)\n",
    "    ins_ID_col= df[ 'AERONET_Instrument_Number']\n",
    "    df.insert(loc=idx, column='instrument_ID', value=ins_ID_col)\n",
    "    elev_col =df['Site_Elevation(m)']\n",
    "    df.insert(loc=idx, column='Elevation(m)', value=elev_col)\n",
    "    lon_col =df[ 'Site_Longitude(Degrees)']\n",
    "    df.insert(loc=idx, column='Longitude', value=lon_col)\n",
    "    lat_col =df['Site_Latitude(Degrees)']\n",
    "    df.insert(loc=idx, column='Latitude', value=lat_col)\n",
    "    site_col =df['AERONET_Site_Name' ]\n",
    "    df.insert(loc=idx, column='Name', value=site_col)\n",
    "    if (df[\"Elevation(m)\"][0]>= 800) & (df['Longitude'][0]<=-60.0):\n",
    "        df.insert(loc=idx, column='Andes?', value=in_Andes)\n",
    "    else:\n",
    "        df.insert(loc=idx, column='Andes?', value=not_Andes)\n",
    "    #dropping columns and rows with all NAN values\n",
    "    df = (df.dropna(axis=1, how='all')\n",
    "                .dropna(axis=0, how='all')\n",
    "                .rename(columns={'Last_Processing_Date(dd/mm/yyyy)': 'Last_Processing_Date'})\n",
    "                .sort_index())\n",
    "    # Assuming df has a date column of type `datetime`\n",
    "    df['season'] = df.datetime.map(season_of_date)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function takes only necessary rows from Aeronet file\n",
    "#it returns a dataframe with daily avarages for the AOD (400-600) nm columns.....maybe just 550\n",
    "#it passes a figure with (time, by wavelength)\n",
    "#\n",
    "\n",
    "def aeronet_timeseries(df):\n",
    "    cols = ['Andes?', 'Name','Country','Latitude','Longitude','Elevation(m)','instrument_ID','datetime','AOD_870nm','AOD_675nm','AOD_500nm',\n",
    "        'AOD_440nm','season','440-870_Angstrom_Exponent','380-500_Angstrom_Exponent', '440-675_Angstrom_Exponent','500-870_Angstrom_Exponent', '340-440_Angstrom_Exponent',\n",
    "        '440-675_Angstrom_Exponent[Polar]', 'Data_Quality_Level'] \n",
    "    df = df.filter(cols, axis=1)  \n",
    "  \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function gets lists of dfs to concantate\n",
    "# it uses aeronet_timeseries function to get the specified columns\n",
    "# it creates/returns daily mean dataframes(dfs), monthly dfs, and seaonal dfs, index by site name, season, and and in Andes or not\n",
    "# cdf= concactatnated df\n",
    "\n",
    "def mean_cdfs(list_dfs):\n",
    " \n",
    "   # df_info = []\n",
    "   # for df in list_dfs:\n",
    "   #     df_info.append({'site': df['Name'][0], 'first_date': df['datetime'][0], 'last_date':df['datetime'].iloc[-1],'Andes?':df['Andes?'][0]})\n",
    "\n",
    "   # df_info=pd.DataFrame(df_info)\n",
    "    \n",
    "    conc_df =pd.concat(list_dfs, ignore_index=True)\n",
    "    conc_df=aeronet_timeseries(conc_df)\n",
    "    #gets indexed datetime to run the upcoming functions of aggregration\n",
    "    conc_df= conc_df.set_index(pd.DatetimeIndex(conc_df['datetime']))\n",
    "    #creating daily dataframe from the concatanated df\n",
    "    daily_cdf = conc_df.groupby([pd.Grouper(freq = \"D\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "    daily_cdf= daily_cdf.reset_index()\n",
    "    #creating weekly dataframe from the concatanated df\n",
    "    weekly_cdf = conc_df.groupby([pd.Grouper(freq = \"W\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "    weekly_cdf= weekly_cdf.reset_index()    \n",
    "    #creating monthly dataframe from the concatanated df\n",
    "    monthly_cdf = conc_df.groupby([pd.Grouper(freq = \"M\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "    monthly_cdf= monthly_cdf.reset_index()\n",
    "     \n",
    "    \n",
    "    return daily_cdf, weekly_cdf, monthly_cdf #, df_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "###this function creates AERONET seasonal means\n",
    "## input: concatanated daily dataframes for all South American AERONET\n",
    "## output: two datframes, one for dry season and one for the wet season averages \n",
    "\n",
    "\n",
    "def AERONET_seasonal_means(conc_daily_df, year):\n",
    "    #this selects the daily measurements for the two seasonal time periods\n",
    "    df_year_wet= daily_ARNET_df[(daily_ARNET_df['datetime'] > str(year) +'-01-15') & (daily_ARNET_df['datetime'] < str(year) +'-05-15')]\n",
    "    df_year_dry= daily_ARNET_df[(daily_ARNET_df['datetime'] > str(year) +'-07-15') & (daily_ARNET_df['datetime'] < str(year) +'-11-15')]\n",
    "\n",
    "    df_year_dry= df_year_dry.set_index(pd.DatetimeIndex(df_year_dry['datetime']))\n",
    "    df_year_wet= df_year_wet.set_index(pd.DatetimeIndex(df_year_wet['datetime']))\n",
    "    seas_year_wet = df_year_wet.groupby([pd.Grouper(freq = \"Y\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "    seas_year_wet= seas_year_wet.reset_index()\n",
    "    seas_year_wet['datetime'] = str(year)+'-01-15'\n",
    "    seas_year_wet['datetime'] =pd.to_datetime(seas_year_wet['datetime'])\n",
    "\n",
    "    df_year_dry= df_year_dry.set_index(pd.DatetimeIndex(df_year_dry['datetime']))\n",
    "    seas_year_dry = df_year_dry.groupby([pd.Grouper(freq = \"Y\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "    seas_year_dry= seas_year_dry.reset_index()\n",
    "    seas_year_dry['datetime'] = str(year) +'-07-15'\n",
    "    seas_year_dry['datetime'] =pd.to_datetime(seas_year_dry['datetime'])\n",
    "\n",
    "    return seas_year_wet, seas_year_dry \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this function creates the seasonal and monthly averages for any GC output variable\n",
    "#input: the GEOS-Chem dataset, variable chosen\n",
    "#output: a xarray dataset for dry, wet, and monthly means\n",
    "def GC_variable_means(GC_dt, VAR):\n",
    "\n",
    "    lat = GC_dt['lat'].values\n",
    "    lon = GC_dt['lon'].values\n",
    "\n",
    "    #extracting the year for the corresponding GC file\n",
    "    year = pd.to_datetime(GC_dt.time[1].values.tolist()).year\n",
    "    #creating the dates for the season for the coresponding file\n",
    "    wet_startdt, wet_enddt, dry_startdt, dry_enddt = [str(year) + '-01-15',str(year) + '-05-15',\n",
    "                                         str(year) + '-07-15', str(year) + '-11-15']\n",
    "\n",
    "    #split xarray with a the time period specified\n",
    "    dry_VAR_time=GC_dt[VAR].sel(time=slice(dry_startdt, dry_enddt))\n",
    "    wet_VAR_time=GC_dt[VAR].sel(time=slice(wet_startdt, wet_enddt))\n",
    "    \n",
    "    # AOD averages per season\n",
    "    # Xarray dataset wfor dry season\n",
    "   # VAR_dry_avg = xr.Dataset(\n",
    "   #     { VAR: ((\"lat\", \"lon\"), dry_VAR_time.mean(dim=('time'))),  },\n",
    "   #     coords={\"lat\": lat, \"lon\": lon},  attrs={\"units\":'AOD'}\n",
    "   # )\n",
    "    VAR_dry_avg =xr.Dataset({\n",
    "        VAR: xr.DataArray(\n",
    "                data =dry_VAR_time.mean(dim=('time')),\n",
    "                coords={\"lat\": lat, \"lon\": lon},\n",
    "                attrs={\"units\":'AOD'})\n",
    "    })\n",
    "    #exanpding the dimension\n",
    "    VAR_dry_avg=VAR_dry_avg.expand_dims('time').assign_coords(new_dim=(\"time\", pd.to_datetime([dry_startdt])))\n",
    "    VAR_dry_avg=VAR_dry_avg.rename({\"new_dim\":'time'})\n",
    "\n",
    "    # Xarray dataset for wet season\n",
    "    #VAR_wet_avg = xr.Dataset(\n",
    "     #   { VAR: ((\"lat\", \"lon\"), wet_VAR_time.mean(dim=('time'))) },\n",
    "      #  coords={\"lat\": lat, \"lon\": lon},  attrs={\"units\":'AOD'}\n",
    "    #)\n",
    "    VAR_wet_avg =xr.Dataset({\n",
    "        VAR: xr.DataArray(\n",
    "                data =wet_VAR_time.mean(dim=('time')),\n",
    "                coords={\"lat\": lat, \"lon\": lon},\n",
    "                attrs={\"units\":'AOD'})\n",
    "    })\n",
    "    #examding dimension\n",
    "    VAR_wet_avg=VAR_wet_avg.expand_dims('time').assign_coords(new_dim=(\"time\", pd.to_datetime([wet_startdt])))\n",
    "    VAR_wet_avg=VAR_wet_avg.rename({\"new_dim\":'time'})\n",
    "    \n",
    "    #calculate PM2.5 monthly averages\n",
    "    VAR_surf_monthly=xr.Dataset()\n",
    "    VAR_surf_monthly=GC_dt[[VAR]].resample(time='1MS').mean()\n",
    "    VAR_surf_monthly.attrs['units']='AOD'\n",
    "\n",
    "    return VAR_wet_avg, VAR_dry_avg, VAR_surf_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this function adds up all the GEOS-Chem AOD components for corresponding wavelengths\n",
    "#input: GEOS-CHEM Aerosols file\n",
    "#output: A GEOS-CHEM dataset with all AOD components, total AOD, and AOD Dust\n",
    "\n",
    "def Aerosols_2_AOD(GC_filename):\n",
    "    Aers_col=xr.open_dataset(GC_filename)\n",
    "    \n",
    "    #500 nm wavelength\n",
    "    Aers_col['AOD500nm_total']=(Aers_col['AODPolarStratCloud500nm'][:,:,:]+Aers_col['AODStratLiquidAer500nm'][:,:,:]+Aers_col['AODSOAfromAqIsoprene500nm'][:,:,:]+Aers_col['AODHyg500nm_SO4'][:,:,:]+\n",
    "                       Aers_col['AODHyg500nm_SALC'][:,:,:]+Aers_col['AODHyg500nm_SALA'][:,:,:]+Aers_col['AODHyg500nm_OCPI'][:,:,:]+Aers_col['AODHyg500nm_BCPI'][:,:,:]+Aers_col['AODDust500nm_bin1'][:,:,:]+Aers_col['AODDust500nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust500nm_bin3'][:,:,:]+Aers_col['AODDust500nm_bin4'][:,:,:]+Aers_col['AODDust500nm_bin5'][:,:,:]+Aers_col['AODDust500nm_bin6'][:,:,:]+Aers_col['AODDust_500_bin7'][:,:,:])\n",
    "    Aers_col['AODDust_500nm']=(Aers_col['AODDust500nm_bin1'][:,:,:]+Aers_col['AODDust500nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust500nm_bin3'][:,:,:]+Aers_col['AODDust500nm_bin4'][:,:,:]+Aers_col['AODDust500nm_bin5'][:,:,:]+Aers_col['AODDust500nm_bin6'][:,:,:]+Aers_col['AODDust_500_bin7'][:,:,:])\n",
    "    \n",
    "    #532 nm wavelength\n",
    "    Aers_col['AOD532nm_total']=(Aers_col['AODPolarStratCloud532nm'][:,:,:]+Aers_col['AODStratLiquidAer532nm'][:,:,:]+Aers_col['AODSOAfromAqIsoprene532nm'][:,:,:]+Aers_col['AODHyg532nm_SO4'][:,:,:]+\n",
    "                       Aers_col['AODHyg532nm_SALC'][:,:,:]+Aers_col['AODHyg532nm_SALA'][:,:,:]+Aers_col['AODHyg532nm_OCPI'][:,:,:]+Aers_col['AODHyg532nm_BCPI'][:,:,:]+Aers_col['AODDust532nm_bin1'][:,:,:]+Aers_col['AODDust532nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust532nm_bin3'][:,:,:]+Aers_col['AODDust532nm_bin4'][:,:,:]+Aers_col['AODDust532nm_bin5'][:,:,:]+Aers_col['AODDust532nm_bin6'][:,:,:]+Aers_col['AODDust532nm_bin7'][:,:,:])\n",
    "    Aers_col['AODDust_532nm']=(Aers_col['AODDust532nm_bin1'][:,:,:]+Aers_col['AODDust532nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust532nm_bin3'][:,:,:]+Aers_col['AODDust532nm_bin4'][:,:,:]+Aers_col['AODDust532nm_bin5'][:,:,:]+Aers_col['AODDust532nm_bin6'][:,:,:]+Aers_col['AODDust532nm_bin7'][:,:,:])\n",
    "    \n",
    "    #550 nm wavelength\n",
    "    Aers_col['AOD550nm_total']=(Aers_col['AODPolarStratCloud550nm'][:,:,:]+Aers_col['AODStratLiquidAer550nm'][:,:,:]+Aers_col['AODSOAfromAqIsoprene550nm'][:,:,:]+Aers_col['AODHyg550nm_SO4'][:,:,:]+\n",
    "                       Aers_col['AODHyg550nm_SALC'][:,:,:]+Aers_col['AODHyg550nm_SALA'][:,:,:]+Aers_col['AODHyg550nm_OCPI'][:,:,:]+Aers_col['AODHyg550nm_BCPI'][:,:,:]+Aers_col['AODDust550nm_bin1'][:,:,:]+Aers_col['AODDust550nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust550nm_bin3'][:,:,:]+Aers_col['AODDust550nm_bin4'][:,:,:]+Aers_col['AODDust550nm_bin5'][:,:,:]+Aers_col['AODDust550nm_bin6'][:,:,:]+Aers_col['AODDust550mn_bin7'][:,:,:])\n",
    "    Aers_col['AODDust_532nm']=(Aers_col['AODDust550nm_bin1'][:,:,:]+Aers_col['AODDust550nm_bin2'][:,:,:]\n",
    "                           +Aers_col['AODDust550nm_bin3'][:,:,:]+Aers_col['AODDust550nm_bin4'][:,:,:]+Aers_col['AODDust550nm_bin5'][:,:,:]+Aers_col['AODDust550nm_bin6'][:,:,:]+Aers_col['AODDust550mn_bin7'][:,:,:])\n",
    "    \n",
    "    return Aers_col\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Uploading and creating GEOS-Chem \n",
    "\n",
    "file_loc_A2014_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2014.nc4\"\n",
    "\n",
    "file_loc_A2015_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2015.nc4\"\n",
    "\n",
    "file_loc_A2016_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2016.nc4\"\n",
    "\n",
    "file_loc_A2017_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2017.nc4\"\n",
    "\n",
    "file_loc_A2018_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2018.nc4\"\n",
    "\n",
    "file_loc_A2019_col= \"/n/mickley/users/ebonilla/Aerosol_col/GEOSChem.Aerosols_Col.2019.nc4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c9a2a4280>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AOD_2014_dt = Aerosols_2_AOD(file_loc_A2014_col)\n",
    "AOD500_wet2014, AOD500_dry2014, AOD500_mon2014 = GC_variable_means(AOD_2014_dt, 'AOD500nm_total')\n",
    "\n",
    "AOD_2015_dt = Aerosols_2_AOD(file_loc_A2015_col)\n",
    "AOD500_wet2015, AOD500_dry2015, AOD500_mon2015 = GC_variable_means(AOD_2015_dt, 'AOD500nm_total')\n",
    "\n",
    "AOD_2016_dt = Aerosols_2_AOD(file_loc_A2016_col)\n",
    "AOD500_wet2016, AOD500_dry2016, AOD500_mon2016 = GC_variable_means(AOD_2016_dt, 'AOD500nm_total')\n",
    "\n",
    "AOD_2017_dt = Aerosols_2_AOD(file_loc_A2017_col)\n",
    "AOD500_wet2017, AOD500_dry2017, AOD500_mon2017 = GC_variable_means(AOD_2017_dt, 'AOD500nm_total')\n",
    "\n",
    "AOD_2018_dt = Aerosols_2_AOD(file_loc_A2018_col)\n",
    "AOD500_wet2018, AOD500_dry2018, AOD500_mon2018 = GC_variable_means(AOD_2018_dt, 'AOD500nm_total')\n",
    "\n",
    "#\n",
    "AOD_2019_dt = Aerosols_2_AOD(file_loc_A2019_col)\n",
    "AOD500_wet2019, AOD500_dry2019, AOD500_mon2019 = GC_variable_means(AOD_2019_dt, 'AOD500nm_total')\n",
    "\n",
    "AOD500_dry2014['AOD500nm_total'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (lat: 151, lon: 89, time: 12)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "  * time            (time) datetime64[ns] 2014-01-15 2014-07-15 ... 2019-07-15\n",
       "Data variables:\n",
       "    AOD500nm_total  (time, lat, lon) float32 0.113 0.1132 ... 0.2908 0.2904</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a9f62fb5-018d-4c4c-ba6b-321d1f6c75d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9f62fb5-018d-4c4c-ba6b-321d1f6c75d1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 151</li><li><span class='xr-has-index'>lon</span>: 89</li><li><span class='xr-has-index'>time</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db558e63-adc8-4799-8d2b-42263432174c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db558e63-adc8-4799-8d2b-42263432174c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -59.5 -59.0 ... 14.5 15.0</div><input id='attrs-a12e8b08-6dba-492b-ae5a-f188f01166fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a12e8b08-6dba-492b-ae5a-f188f01166fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7966f9b7-3739-40a0-93a7-e6663b7d55fb' class='xr-var-data-in' type='checkbox'><label for='data-7966f9b7-3739-40a0-93a7-e6663b7d55fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,\n",
       "       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,\n",
       "       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,\n",
       "       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,\n",
       "       -40. , -39.5, -39. , -38.5, -38. , -37.5, -37. , -36.5, -36. , -35.5,\n",
       "       -35. , -34.5, -34. , -33.5, -33. , -32.5, -32. , -31.5, -31. , -30.5,\n",
       "       -30. , -29.5, -29. , -28.5, -28. , -27.5, -27. , -26.5, -26. , -25.5,\n",
       "       -25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-85.0 -84.38 ... -30.62 -30.0</div><input id='attrs-144a4801-66df-4d35-b644-89ae3502a494' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-144a4801-66df-4d35-b644-89ae3502a494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6af8473-fa44-4cf8-8529-7e6164790b0d' class='xr-var-data-in' type='checkbox'><label for='data-f6af8473-fa44-4cf8-8529-7e6164790b0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-85.   , -84.375, -83.75 , -83.125, -82.5  , -81.875, -81.25 , -80.625,\n",
       "       -80.   , -79.375, -78.75 , -78.125, -77.5  , -76.875, -76.25 , -75.625,\n",
       "       -75.   , -74.375, -73.75 , -73.125, -72.5  , -71.875, -71.25 , -70.625,\n",
       "       -70.   , -69.375, -68.75 , -68.125, -67.5  , -66.875, -66.25 , -65.625,\n",
       "       -65.   , -64.375, -63.75 , -63.125, -62.5  , -61.875, -61.25 , -60.625,\n",
       "       -60.   , -59.375, -58.75 , -58.125, -57.5  , -56.875, -56.25 , -55.625,\n",
       "       -55.   , -54.375, -53.75 , -53.125, -52.5  , -51.875, -51.25 , -50.625,\n",
       "       -50.   , -49.375, -48.75 , -48.125, -47.5  , -46.875, -46.25 , -45.625,\n",
       "       -45.   , -44.375, -43.75 , -43.125, -42.5  , -41.875, -41.25 , -40.625,\n",
       "       -40.   , -39.375, -38.75 , -38.125, -37.5  , -36.875, -36.25 , -35.625,\n",
       "       -35.   , -34.375, -33.75 , -33.125, -32.5  , -31.875, -31.25 , -30.625,\n",
       "       -30.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2014-01-15 ... 2019-07-15</div><input id='attrs-c242104c-4dc0-4b22-bf93-fb01e2806b9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c242104c-4dc0-4b22-bf93-fb01e2806b9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08b77fc5-22cf-4381-a6cc-22d05c8a9a16' class='xr-var-data-in' type='checkbox'><label for='data-08b77fc5-22cf-4381-a6cc-22d05c8a9a16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2014-01-15T00:00:00.000000000&#x27;, &#x27;2014-07-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-15T00:00:00.000000000&#x27;, &#x27;2015-07-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-15T00:00:00.000000000&#x27;, &#x27;2016-07-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-15T00:00:00.000000000&#x27;, &#x27;2017-07-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-15T00:00:00.000000000&#x27;, &#x27;2018-07-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-15T00:00:00.000000000&#x27;, &#x27;2019-07-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4932664d-f1f6-4ae9-8c88-a57c6569c581' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4932664d-f1f6-4ae9-8c88-a57c6569c581' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AOD500nm_total</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.113 0.1132 ... 0.2908 0.2904</div><input id='attrs-d8e41925-fe67-4761-91a5-4186b0679bb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8e41925-fe67-4761-91a5-4186b0679bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e8f5ef8-fefa-4e9d-b5a2-8583b9bebae1' class='xr-var-data-in' type='checkbox'><label for='data-8e8f5ef8-fefa-4e9d-b5a2-8583b9bebae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>AOD</dd></dl></div><div class='xr-var-data'><pre>array([[[0.11295333, 0.1131908 , 0.11330657, ..., 0.06241602,\n",
       "         0.06233943, 0.06227618],\n",
       "        [0.11381103, 0.14956856, 0.14820935, ..., 0.07474022,\n",
       "         0.07428648, 0.06264164],\n",
       "        [0.11960523, 0.12168694, 0.12123591, ..., 0.06901202,\n",
       "         0.09958326, 0.06798952],\n",
       "        ...,\n",
       "        [0.08868513, 0.09094909, 0.11488835, ..., 0.20377822,\n",
       "         0.11791054, 0.2048027 ],\n",
       "        [0.08987183, 0.08389001, 0.10321563, ..., 0.14341356,\n",
       "         0.14476304, 0.20551023],\n",
       "        [0.09286115, 0.09669625, 0.11481331, ..., 0.1961382 ,\n",
       "         0.19775951, 0.19635397]],\n",
       "\n",
       "       [[0.10036706, 0.10065538, 0.1010177 , ..., 0.03930525,\n",
       "         0.03896432, 0.03887346],\n",
       "        [0.10105028, 0.13403411, 0.13399504, ..., 0.04077707,\n",
       "         0.03975146, 0.03900166],\n",
       "        [0.10439471, 0.10557529, 0.10578558, ..., 0.04654057,\n",
       "         0.04403779, 0.04504992],\n",
       "...\n",
       "        [0.09519526, 0.09731144, 0.11580861, ..., 0.17915457,\n",
       "         0.10017101, 0.18214768],\n",
       "        [0.09573851, 0.08892659, 0.10267431, ..., 0.126307  ,\n",
       "         0.12753837, 0.18258476],\n",
       "        [0.09690312, 0.10129796, 0.11653668, ..., 0.16970752,\n",
       "         0.17246091, 0.1714783 ]],\n",
       "\n",
       "       [[0.14606218, 0.14641932, 0.14770527, ..., 0.05991093,\n",
       "         0.05891713, 0.05898933],\n",
       "        [0.14694348, 0.22263022, 0.22380848, ..., 0.07226735,\n",
       "         0.06955379, 0.06056869],\n",
       "        [0.15376133, 0.15513022, 0.15467499, ..., 0.07857598,\n",
       "         0.0821802 , 0.07576939],\n",
       "        ...,\n",
       "        [0.092288  , 0.09144332, 0.11357924, ..., 0.2645372 ,\n",
       "         0.2600568 , 0.26769072],\n",
       "        [0.09244765, 0.0923417 , 0.10795173, ..., 0.22522734,\n",
       "         0.22928292, 0.26730174],\n",
       "        [0.0943395 , 0.09640124, 0.11421343, ..., 0.2837627 ,\n",
       "         0.29081506, 0.29036418]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a123e280-3577-49c2-a9e6-1dc6ac964968' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a123e280-3577-49c2-a9e6-1dc6ac964968' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lat: 151, lon: 89, time: 12)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "  * time            (time) datetime64[ns] 2014-01-15 2014-07-15 ... 2019-07-15\n",
       "Data variables:\n",
       "    AOD500nm_total  (time, lat, lon) float32 0.113 0.1132 ... 0.2908 0.2904"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##concatenating the AOD from GEOS-Chem xarray datasets for the seasons\n",
    "AOD_2014_2019_dt = xr.concat([AOD500_wet2014, AOD500_dry2014, AOD500_wet2015,AOD500_dry2015, AOD500_wet2016, \n",
    "                             AOD500_dry2016, AOD500_wet2017, AOD500_dry2017, AOD500_wet2018, AOD500_dry2018,\n",
    "                              AOD500_wet2019,AOD500_dry2019 ], dim='time')\n",
    "AOD_2014_2019_wet = xr.concat([AOD500_wet2014,  AOD500_wet2015, AOD500_wet2016, \n",
    "                              AOD500_wet2017,  AOD500_wet2018, \n",
    "                              AOD500_wet2019 ], dim='time').mean(dim='time')\n",
    "\n",
    "\n",
    "\n",
    "AOD_2014_2019_dry = xr.concat([ AOD500_dry2014, AOD500_dry2015,  \n",
    "                             AOD500_dry2016,  AOD500_dry2017,  AOD500_dry2018,\n",
    "                             AOD500_dry2019 ], dim='time').mean(dim='time')\n",
    "\n",
    "\n",
    "\n",
    "AOD_2014_2019_dt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "Data variables:\n",
       "    AOD500nm_total  (lat, lon) float32 0.1222 0.1223 0.1226 ... 0.211 0.2096</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6add4e30-cf03-4f5c-b0ce-b5421c503a59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6add4e30-cf03-4f5c-b0ce-b5421c503a59' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 151</li><li><span class='xr-has-index'>lon</span>: 89</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e051cc20-f994-4f90-a5f4-be9965311010' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e051cc20-f994-4f90-a5f4-be9965311010' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -59.5 -59.0 ... 14.5 15.0</div><input id='attrs-24a3eb5f-3212-4db6-82c4-70f8e762f9c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24a3eb5f-3212-4db6-82c4-70f8e762f9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa1af56-ef54-44b9-bd25-7fbbcaf49df0' class='xr-var-data-in' type='checkbox'><label for='data-9aa1af56-ef54-44b9-bd25-7fbbcaf49df0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,\n",
       "       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,\n",
       "       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,\n",
       "       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,\n",
       "       -40. , -39.5, -39. , -38.5, -38. , -37.5, -37. , -36.5, -36. , -35.5,\n",
       "       -35. , -34.5, -34. , -33.5, -33. , -32.5, -32. , -31.5, -31. , -30.5,\n",
       "       -30. , -29.5, -29. , -28.5, -28. , -27.5, -27. , -26.5, -26. , -25.5,\n",
       "       -25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-85.0 -84.38 ... -30.62 -30.0</div><input id='attrs-93d8dadb-6dfb-4b00-9b1f-89c5b9c38660' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93d8dadb-6dfb-4b00-9b1f-89c5b9c38660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1884f960-8945-439a-8a08-f5cd54cb22ce' class='xr-var-data-in' type='checkbox'><label for='data-1884f960-8945-439a-8a08-f5cd54cb22ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-85.   , -84.375, -83.75 , -83.125, -82.5  , -81.875, -81.25 , -80.625,\n",
       "       -80.   , -79.375, -78.75 , -78.125, -77.5  , -76.875, -76.25 , -75.625,\n",
       "       -75.   , -74.375, -73.75 , -73.125, -72.5  , -71.875, -71.25 , -70.625,\n",
       "       -70.   , -69.375, -68.75 , -68.125, -67.5  , -66.875, -66.25 , -65.625,\n",
       "       -65.   , -64.375, -63.75 , -63.125, -62.5  , -61.875, -61.25 , -60.625,\n",
       "       -60.   , -59.375, -58.75 , -58.125, -57.5  , -56.875, -56.25 , -55.625,\n",
       "       -55.   , -54.375, -53.75 , -53.125, -52.5  , -51.875, -51.25 , -50.625,\n",
       "       -50.   , -49.375, -48.75 , -48.125, -47.5  , -46.875, -46.25 , -45.625,\n",
       "       -45.   , -44.375, -43.75 , -43.125, -42.5  , -41.875, -41.25 , -40.625,\n",
       "       -40.   , -39.375, -38.75 , -38.125, -37.5  , -36.875, -36.25 , -35.625,\n",
       "       -35.   , -34.375, -33.75 , -33.125, -32.5  , -31.875, -31.25 , -30.625,\n",
       "       -30.   ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb931e18-63a4-4078-9386-5e95db811e4b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb931e18-63a4-4078-9386-5e95db811e4b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AOD500nm_total</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1222 0.1223 ... 0.211 0.2096</div><input id='attrs-89e7e13f-cdc9-4a74-a322-e7632b566871' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89e7e13f-cdc9-4a74-a322-e7632b566871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba88df6f-4855-41c7-b73d-7768c0ce5c9e' class='xr-var-data-in' type='checkbox'><label for='data-ba88df6f-4855-41c7-b73d-7768c0ce5c9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12224906, 0.12234823, 0.12258459, ..., 0.07374658, 0.07399426,\n",
       "        0.07402419],\n",
       "       [0.12276418, 0.15091334, 0.15088733, ..., 0.094585  , 0.09456521,\n",
       "        0.07428288],\n",
       "       [0.1269836 , 0.12872228, 0.1285738 , ..., 0.08047394, 0.11908259,\n",
       "        0.0796856 ],\n",
       "       ...,\n",
       "       [0.09240577, 0.09382968, 0.11598089, ..., 0.21836126, 0.13235877,\n",
       "        0.22111313],\n",
       "       [0.09329506, 0.08929495, 0.10634654, ..., 0.16298757, 0.16441825,\n",
       "        0.22149082],\n",
       "       [0.09681378, 0.10005168, 0.11712837, ..., 0.20752412, 0.21099903,\n",
       "        0.2096352 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09078698-7553-43c1-9213-f70c13f8c6e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-09078698-7553-43c1-9213-f70c13f8c6e7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "Data variables:\n",
       "    AOD500nm_total  (lat, lon) float32 0.1222 0.1223 0.1226 ... 0.211 0.2096"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AOD_2014_2019_wet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (time: 72, lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2014-01-01 2014-02-01 ... 2019-12-01\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "Data variables:\n",
       "    AOD500nm_total  (time, lat, lon) float32 0.09726 0.09747 ... 0.4582 0.4545\n",
       "Attributes:\n",
       "    units:    AOD</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9e6fed6c-b463-4927-a464-0279fb17777e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9e6fed6c-b463-4927-a464-0279fb17777e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 72</li><li><span class='xr-has-index'>lat</span>: 151</li><li><span class='xr-has-index'>lon</span>: 89</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b2a330ed-29a8-41d4-8f46-a29d0f4ae7ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2a330ed-29a8-41d4-8f46-a29d0f4ae7ea' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2014-01-01 ... 2019-12-01</div><input id='attrs-492f8bb1-f8d3-48b1-bb05-8563344c3f57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-492f8bb1-f8d3-48b1-bb05-8563344c3f57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f76adcbd-23ba-42cb-9539-67b6596a02c5' class='xr-var-data-in' type='checkbox'><label for='data-f76adcbd-23ba-42cb-9539-67b6596a02c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2014-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-03-01T00:00:00.000000000&#x27;, &#x27;2014-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-05-01T00:00:00.000000000&#x27;, &#x27;2014-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-01T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-09-01T00:00:00.000000000&#x27;, &#x27;2014-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-01T00:00:00.000000000&#x27;, &#x27;2014-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-01T00:00:00.000000000&#x27;, &#x27;2015-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-01T00:00:00.000000000&#x27;, &#x27;2015-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-01T00:00:00.000000000&#x27;, &#x27;2015-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-01T00:00:00.000000000&#x27;, &#x27;2015-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-01T00:00:00.000000000&#x27;, &#x27;2016-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-01T00:00:00.000000000&#x27;, &#x27;2016-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-01T00:00:00.000000000&#x27;, &#x27;2016-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2017-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-01T00:00:00.000000000&#x27;, &#x27;2017-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-01T00:00:00.000000000&#x27;, &#x27;2017-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-01T00:00:00.000000000&#x27;, &#x27;2017-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-01T00:00:00.000000000&#x27;, &#x27;2017-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-01T00:00:00.000000000&#x27;, &#x27;2018-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T00:00:00.000000000&#x27;, &#x27;2018-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-01T00:00:00.000000000&#x27;, &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2019-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-01T00:00:00.000000000&#x27;, &#x27;2019-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-01T00:00:00.000000000&#x27;, &#x27;2019-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-01T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-01T00:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-85.0 -84.38 ... -30.62 -30.0</div><input id='attrs-87508bda-37e3-48bc-99ce-529a9a5de7bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87508bda-37e3-48bc-99ce-529a9a5de7bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45ae6eff-60ff-4e21-9efb-db7a95fd4115' class='xr-var-data-in' type='checkbox'><label for='data-45ae6eff-60ff-4e21-9efb-db7a95fd4115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-85.   , -84.375, -83.75 , -83.125, -82.5  , -81.875, -81.25 , -80.625,\n",
       "       -80.   , -79.375, -78.75 , -78.125, -77.5  , -76.875, -76.25 , -75.625,\n",
       "       -75.   , -74.375, -73.75 , -73.125, -72.5  , -71.875, -71.25 , -70.625,\n",
       "       -70.   , -69.375, -68.75 , -68.125, -67.5  , -66.875, -66.25 , -65.625,\n",
       "       -65.   , -64.375, -63.75 , -63.125, -62.5  , -61.875, -61.25 , -60.625,\n",
       "       -60.   , -59.375, -58.75 , -58.125, -57.5  , -56.875, -56.25 , -55.625,\n",
       "       -55.   , -54.375, -53.75 , -53.125, -52.5  , -51.875, -51.25 , -50.625,\n",
       "       -50.   , -49.375, -48.75 , -48.125, -47.5  , -46.875, -46.25 , -45.625,\n",
       "       -45.   , -44.375, -43.75 , -43.125, -42.5  , -41.875, -41.25 , -40.625,\n",
       "       -40.   , -39.375, -38.75 , -38.125, -37.5  , -36.875, -36.25 , -35.625,\n",
       "       -35.   , -34.375, -33.75 , -33.125, -32.5  , -31.875, -31.25 , -30.625,\n",
       "       -30.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -59.5 -59.0 ... 14.5 15.0</div><input id='attrs-6dc4cc49-276c-414e-ba75-4486719cb864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dc4cc49-276c-414e-ba75-4486719cb864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5f806a2-41a0-4eab-b12d-44e8d894dea1' class='xr-var-data-in' type='checkbox'><label for='data-e5f806a2-41a0-4eab-b12d-44e8d894dea1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,\n",
       "       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,\n",
       "       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,\n",
       "       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,\n",
       "       -40. , -39.5, -39. , -38.5, -38. , -37.5, -37. , -36.5, -36. , -35.5,\n",
       "       -35. , -34.5, -34. , -33.5, -33. , -32.5, -32. , -31.5, -31. , -30.5,\n",
       "       -30. , -29.5, -29. , -28.5, -28. , -27.5, -27. , -26.5, -26. , -25.5,\n",
       "       -25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e1da0b6-4cad-4b51-b94f-69bcf73de2a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e1da0b6-4cad-4b51-b94f-69bcf73de2a7' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AOD500nm_total</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.09726 0.09747 ... 0.4582 0.4545</div><input id='attrs-48ae363e-3b1b-4f18-8fd6-63cecf47b78c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48ae363e-3b1b-4f18-8fd6-63cecf47b78c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e45a4460-4fa2-45f4-b911-d0526735d4cb' class='xr-var-data-in' type='checkbox'><label for='data-e45a4460-4fa2-45f4-b911-d0526735d4cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.09725601, 0.0974669 , 0.09888686, ..., 0.06188954,\n",
       "         0.06363594, 0.0637013 ],\n",
       "        [0.09797402, 0.12437812, 0.12593608, ..., 0.05918092,\n",
       "         0.0609592 , 0.062875  ],\n",
       "        [0.10540291, 0.10753527, 0.10821021, ..., 0.0664557 ,\n",
       "         0.1110047 , 0.06770412],\n",
       "        ...,\n",
       "        [0.088211  , 0.089266  , 0.1285213 , ..., 0.22935577,\n",
       "         0.1267398 , 0.22108485],\n",
       "        [0.08931965, 0.06542028, 0.08874927, ..., 0.14421165,\n",
       "         0.14133538, 0.22141407],\n",
       "        [0.09816314, 0.10060511, 0.12673268, ..., 0.22895825,\n",
       "         0.2244837 , 0.22235872]],\n",
       "\n",
       "       [[0.11918251, 0.11953092, 0.11826055, ..., 0.07345231,\n",
       "         0.07343476, 0.07344083],\n",
       "        [0.12015072, 0.16343312, 0.16080436, ..., 0.09315961,\n",
       "         0.09179227, 0.07397544],\n",
       "        [0.12834914, 0.13147472, 0.12924828, ..., 0.07969929,\n",
       "         0.13182218, 0.07853232],\n",
       "...\n",
       "        [0.0659912 , 0.06628913, 0.08568522, ..., 0.1344358 ,\n",
       "         0.06584302, 0.13630286],\n",
       "        [0.06539767, 0.06107439, 0.07410695, ..., 0.1083753 ,\n",
       "         0.11228554, 0.13606378],\n",
       "        [0.07257987, 0.0758908 , 0.08976538, ..., 0.12431622,\n",
       "         0.12788087, 0.12669994]],\n",
       "\n",
       "       [[0.16919142, 0.16907379, 0.16808029, ..., 0.09072177,\n",
       "         0.09000894, 0.09036048],\n",
       "        [0.1706822 , 0.1708059 , 0.17028041, ..., 0.09222023,\n",
       "         0.09161098, 0.08975838],\n",
       "        [0.18595518, 0.18749492, 0.1854357 , ..., 0.09627514,\n",
       "         0.13860884, 0.09557374],\n",
       "        ...,\n",
       "        [0.09390933, 0.09539691, 0.13644648, ..., 0.45781785,\n",
       "         0.2122642 , 0.46417007],\n",
       "        [0.09315359, 0.07116819, 0.09384726, ..., 0.3423599 ,\n",
       "         0.34987757, 0.46516603],\n",
       "        [0.10119321, 0.10668592, 0.13586655, ..., 0.44498044,\n",
       "         0.45823553, 0.4545188 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc9a5a22-0af7-414b-afe7-790c10d3b531' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc9a5a22-0af7-414b-afe7-790c10d3b531' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>AOD</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (time: 72, lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2014-01-01 2014-02-01 ... 2019-12-01\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "Data variables:\n",
       "    AOD500nm_total  (time, lat, lon) float32 0.09726 0.09747 ... 0.4582 0.4545\n",
       "Attributes:\n",
       "    units:    AOD"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatanating monhtly GC files\n",
    "AOD_2014_2019_mon= xr.concat([AOD500_mon2014, AOD500_mon2015, \n",
    "                             AOD500_mon2016, AOD500_mon2017, AOD500_mon2018,AOD500_mon2019], dim='time')\n",
    "AOD_2014_2019_mon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(AOD_2014_2019_dt['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "20140715-20141115.84W_56S_33W_14N.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AOD w/ MODIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uploading MODIS files\n",
    "#files are seasonal averages\n",
    "MODIS_2014_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20140115-20140515.84W_56S_33W_14N.nc')\n",
    "MODIS_2014_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20140715-20141115.84W_56S_33W_14N.nc')\n",
    "MODIS_2015_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20150115-20150515.84W_56S_33W_14N.nc')\n",
    "MODIS_2015_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20150715-20151115.84W_56S_33W_14N.nc')\n",
    "MODIS_2016_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20160115-20160515.84W_56S_33W_14N.nc')\n",
    "MODIS_2016_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20160715-20161115.84W_56S_33W_14N.nc')\n",
    "MODIS_2017_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20170115-20170515.84W_56S_33W_14N.nc')\n",
    "MODIS_2017_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20170715-20171115.84W_56S_33W_14N.nc')\n",
    "MODIS_2018_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20180115-20180515.84W_56S_33W_14N.nc')\n",
    "MODIS_2018_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20180715-20181115.84W_56S_33W_14N.nc')\n",
    "MODIS_2019_wet = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20190115-20190515.84W_56S_33W_14N.nc')\n",
    "MODIS_2019_dry = xr.open_dataset('../AQ_Amazon_files/MODIS_means/g4.timeAvgMap.MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean.20190715-20191115.84W_56S_33W_14N.nc')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c9c086b20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MODIS_2019_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Concatanating MODIS AOD\n",
    "MODIS550_2014_2019_wet = xr.concat([MODIS_2014_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],  MODIS_2015_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'], MODIS_2016_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'], \n",
    "                              MODIS_2017_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],  MODIS_2018_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'], \n",
    "                              MODIS_2019_wet['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean']], dim='time').mean(dim='time')\n",
    "\n",
    "MODIS550_2014_2019_dry = xr.concat([MODIS_2014_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'], MODIS_2015_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],  \n",
    "                             MODIS_2016_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],  MODIS_2017_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],  MODIS_2018_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'],\n",
    "                             MODIS_2019_dry['MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean']], dim='time').mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c9c113be0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MODIS550_2014_2019_wet.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<defs>\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "body.vscode-dark {\n",
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       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header {\n",
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       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean&#x27; (lat: 71, lon: 51)&gt;\n",
       "array([[0.10337795, 0.09788569, 0.09399742, ..., 0.0977429 , 0.12606228,\n",
       "        0.1334399 ],\n",
       "       [0.1057817 , 0.09938637, 0.09677585, ..., 0.08382635, 0.08846803,\n",
       "        0.12358657],\n",
       "       [0.11973294, 0.1237521 , 0.12309485, ..., 0.09325553, 0.10144136,\n",
       "        0.11397137],\n",
       "       ...,\n",
       "       [0.22740885, 0.23551205, 0.23362534, ..., 0.32521446, 0.32744973,\n",
       "        0.32711471],\n",
       "       [0.21569323, 0.22461505, 0.21889054, ..., 0.30044786, 0.30210368,\n",
       "        0.30115709],\n",
       "       [0.19469857, 0.21951737, 0.21202866, ..., 0.27114376, 0.29021486,\n",
       "        0.28559328]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -55.5 -54.5 -53.5 -52.5 -51.5 ... 11.5 12.5 13.5 14.5\n",
       "  * lon      (lon) float32 -83.5 -82.5 -81.5 -80.5 ... -36.5 -35.5 -34.5 -33.5</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 71</li><li><span class='xr-has-index'>lon</span>: 51</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b6a53da7-94f7-4c7b-955c-355359259ee6' class='xr-array-in' type='checkbox' checked><label for='section-b6a53da7-94f7-4c7b-955c-355359259ee6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.1034 0.09789 0.094 0.09709 0.1018 ... 0.2766 0.2711 0.2902 0.2856</span></div><div class='xr-array-data'><pre>array([[0.10337795, 0.09788569, 0.09399742, ..., 0.0977429 , 0.12606228,\n",
       "        0.1334399 ],\n",
       "       [0.1057817 , 0.09938637, 0.09677585, ..., 0.08382635, 0.08846803,\n",
       "        0.12358657],\n",
       "       [0.11973294, 0.1237521 , 0.12309485, ..., 0.09325553, 0.10144136,\n",
       "        0.11397137],\n",
       "       ...,\n",
       "       [0.22740885, 0.23551205, 0.23362534, ..., 0.32521446, 0.32744973,\n",
       "        0.32711471],\n",
       "       [0.21569323, 0.22461505, 0.21889054, ..., 0.30044786, 0.30210368,\n",
       "        0.30115709],\n",
       "       [0.19469857, 0.21951737, 0.21202866, ..., 0.27114376, 0.29021486,\n",
       "        0.28559328]])</pre></div></div></li><li class='xr-section-item'><input id='section-5195c685-9b74-464e-aa5a-c9870120500e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5195c685-9b74-464e-aa5a-c9870120500e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-55.5 -54.5 -53.5 ... 13.5 14.5</div><input id='attrs-3f6b0fba-d87f-4ad5-88db-d212fdd16f88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f6b0fba-d87f-4ad5-88db-d212fdd16f88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b882ffd-8a25-4b26-a143-9b8073131c17' class='xr-var-data-in' type='checkbox'><label for='data-3b882ffd-8a25-4b26-a143-9b8073131c17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5,\n",
       "       -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5,\n",
       "       -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5,\n",
       "       -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5,\n",
       "       -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,\n",
       "        -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,\n",
       "         4.5,   5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,\n",
       "        14.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-83.5 -82.5 -81.5 ... -34.5 -33.5</div><input id='attrs-c2ed5019-536e-4130-b247-563e4fe338a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2ed5019-536e-4130-b247-563e4fe338a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1be4ffd3-747f-4ff9-acd3-ac2e76a357e7' class='xr-var-data-in' type='checkbox'><label for='data-1be4ffd3-747f-4ff9-acd3-ac2e76a357e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5,\n",
       "       -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5,\n",
       "       -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5,\n",
       "       -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5,\n",
       "       -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5,\n",
       "       -33.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-add81c48-6ebd-4320-8f12-1843bc63f71a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-add81c48-6ebd-4320-8f12-1843bc63f71a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'MYD08_D3_6_1_AOD_550_Dark_Target_Deep_Blue_Combined_Mean' (lat: 71, lon: 51)>\n",
       "array([[0.10337795, 0.09788569, 0.09399742, ..., 0.0977429 , 0.12606228,\n",
       "        0.1334399 ],\n",
       "       [0.1057817 , 0.09938637, 0.09677585, ..., 0.08382635, 0.08846803,\n",
       "        0.12358657],\n",
       "       [0.11973294, 0.1237521 , 0.12309485, ..., 0.09325553, 0.10144136,\n",
       "        0.11397137],\n",
       "       ...,\n",
       "       [0.22740885, 0.23551205, 0.23362534, ..., 0.32521446, 0.32744973,\n",
       "        0.32711471],\n",
       "       [0.21569323, 0.22461505, 0.21889054, ..., 0.30044786, 0.30210368,\n",
       "        0.30115709],\n",
       "       [0.19469857, 0.21951737, 0.21202866, ..., 0.27114376, 0.29021486,\n",
       "        0.28559328]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -55.5 -54.5 -53.5 -52.5 -51.5 ... 11.5 12.5 13.5 14.5\n",
       "  * lon      (lon) float32 -83.5 -82.5 -81.5 -80.5 ... -36.5 -35.5 -34.5 -33.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MODIS550_2014_2019_wet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting GEOS-Chem corresponding AOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c8cb6f250>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#getting GEOS-CHEM AOD at 550\n",
    "\n",
    "AOD550_wet2014, AOD550_dry2014, AOD550_mon2014 = GC_variable_means(AOD_2014_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_wet2015, AOD550_dry2015, AOD550_mon2015 = GC_variable_means(AOD_2015_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_wet2016, AOD550_dry2016, AOD550_mon2016 = GC_variable_means(AOD_2016_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_wet2017, AOD550_dry2017, AOD550_mon2017 = GC_variable_means(AOD_2017_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_wet2018, AOD550_dry2018, AOD550_mon2018 = GC_variable_means(AOD_2018_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_wet2019, AOD550_dry2019, AOD550_mon2019 = GC_variable_means(AOD_2019_dt, 'AOD550nm_total')\n",
    "\n",
    "AOD550_dry2019['AOD550nm_total'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "##concatenating the AOD from GEOS-Chem xarray datasets for the seasons\n",
    "AOD550_2014_2019_dt = xr.concat([AOD550_wet2014, AOD550_dry2014, AOD550_wet2015,AOD550_dry2015, AOD550_wet2016, \n",
    "                             AOD550_dry2016, AOD550_wet2017, AOD550_dry2017, AOD550_wet2018, AOD550_dry2018,\n",
    "                              AOD550_wet2019,AOD550_dry2019 ], dim='time')\n",
    "AOD550_2014_2019_wet = xr.concat([AOD550_wet2014,  AOD550_wet2015, AOD550_wet2016, \n",
    "                              AOD550_wet2017,  AOD550_wet2018, \n",
    "                              AOD550_wet2019 ], dim='time').mean(dim='time')\n",
    "\n",
    "AOD550_2014_2019_dry = xr.concat([ AOD550_dry2014, AOD550_dry2015,  \n",
    "                             AOD550_dry2016,  AOD550_dry2017,  AOD550_dry2018,\n",
    "                             AOD550_dry2019 ], dim='time').mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "Data variables:\n",
       "    AOD550nm_total  (lat, lon) float32 0.118 0.1183 0.1192 ... 0.2743 0.2737</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-298b5c77-7fce-4f49-9086-eab9518e8f1f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-298b5c77-7fce-4f49-9086-eab9518e8f1f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 151</li><li><span class='xr-has-index'>lon</span>: 89</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a46eeba7-54f7-4ba2-add5-2165131a82cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a46eeba7-54f7-4ba2-add5-2165131a82cd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -59.5 -59.0 ... 14.5 15.0</div><input id='attrs-4ef829d4-34d6-478d-8063-6f473f7c4524' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ef829d4-34d6-478d-8063-6f473f7c4524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66927503-4b1f-4762-9549-a6197158dbb5' class='xr-var-data-in' type='checkbox'><label for='data-66927503-4b1f-4762-9549-a6197158dbb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60. , -59.5, -59. , -58.5, -58. , -57.5, -57. , -56.5, -56. , -55.5,\n",
       "       -55. , -54.5, -54. , -53.5, -53. , -52.5, -52. , -51.5, -51. , -50.5,\n",
       "       -50. , -49.5, -49. , -48.5, -48. , -47.5, -47. , -46.5, -46. , -45.5,\n",
       "       -45. , -44.5, -44. , -43.5, -43. , -42.5, -42. , -41.5, -41. , -40.5,\n",
       "       -40. , -39.5, -39. , -38.5, -38. , -37.5, -37. , -36.5, -36. , -35.5,\n",
       "       -35. , -34.5, -34. , -33.5, -33. , -32.5, -32. , -31.5, -31. , -30.5,\n",
       "       -30. , -29.5, -29. , -28.5, -28. , -27.5, -27. , -26.5, -26. , -25.5,\n",
       "       -25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-85.0 -84.38 ... -30.62 -30.0</div><input id='attrs-a25cb72d-490c-43a9-b403-a98293d0ada3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a25cb72d-490c-43a9-b403-a98293d0ada3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-916a9699-2b4d-4b4e-9a7f-b9c03db70ba3' class='xr-var-data-in' type='checkbox'><label for='data-916a9699-2b4d-4b4e-9a7f-b9c03db70ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-85.   , -84.375, -83.75 , -83.125, -82.5  , -81.875, -81.25 , -80.625,\n",
       "       -80.   , -79.375, -78.75 , -78.125, -77.5  , -76.875, -76.25 , -75.625,\n",
       "       -75.   , -74.375, -73.75 , -73.125, -72.5  , -71.875, -71.25 , -70.625,\n",
       "       -70.   , -69.375, -68.75 , -68.125, -67.5  , -66.875, -66.25 , -65.625,\n",
       "       -65.   , -64.375, -63.75 , -63.125, -62.5  , -61.875, -61.25 , -60.625,\n",
       "       -60.   , -59.375, -58.75 , -58.125, -57.5  , -56.875, -56.25 , -55.625,\n",
       "       -55.   , -54.375, -53.75 , -53.125, -52.5  , -51.875, -51.25 , -50.625,\n",
       "       -50.   , -49.375, -48.75 , -48.125, -47.5  , -46.875, -46.25 , -45.625,\n",
       "       -45.   , -44.375, -43.75 , -43.125, -42.5  , -41.875, -41.25 , -40.625,\n",
       "       -40.   , -39.375, -38.75 , -38.125, -37.5  , -36.875, -36.25 , -35.625,\n",
       "       -35.   , -34.375, -33.75 , -33.125, -32.5  , -31.875, -31.25 , -30.625,\n",
       "       -30.   ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cfadc94-95eb-4006-9b6c-8d0e29fcb5a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cfadc94-95eb-4006-9b6c-8d0e29fcb5a3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>AOD550nm_total</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.118 0.1183 ... 0.2743 0.2737</div><input id='attrs-b2bd7939-b4c8-4450-a1f0-4a1bdae02172' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2bd7939-b4c8-4450-a1f0-4a1bdae02172' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db57f83-4386-4a8e-88ea-9a018b4251d0' class='xr-var-data-in' type='checkbox'><label for='data-1db57f83-4386-4a8e-88ea-9a018b4251d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.11804465, 0.11829939, 0.11916273, ..., 0.04690802, 0.04619397,\n",
       "        0.04625946],\n",
       "       [0.11888378, 0.1607665 , 0.16111322, ..., 0.05445929, 0.05283232,\n",
       "        0.04691686],\n",
       "       [0.12376431, 0.12485159, 0.12534781, ..., 0.05878979, 0.06182292,\n",
       "        0.05688718],\n",
       "       ...,\n",
       "       [0.08707777, 0.0866042 , 0.11411343, ..., 0.25020835, 0.2535001 ,\n",
       "        0.25455764],\n",
       "       [0.08717578, 0.08348418, 0.10306907, ..., 0.21658535, 0.22167265,\n",
       "        0.25410205],\n",
       "       [0.08954453, 0.09181584, 0.11241438, ..., 0.2667562 , 0.2742877 ,\n",
       "        0.2736955 ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eca60545-b5a4-4733-8ec6-40321862ca46' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eca60545-b5a4-4733-8ec6-40321862ca46' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lat: 151, lon: 89)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -60.0 -59.5 -59.0 -58.5 ... 14.0 14.5 15.0\n",
       "  * lon             (lon) float64 -85.0 -84.38 -83.75 ... -31.25 -30.62 -30.0\n",
       "Data variables:\n",
       "    AOD550nm_total  (lat, lon) float32 0.118 0.1183 0.1192 ... 0.2743 0.2737"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AOD550_2014_2019_dry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imin = index.get_loc(minval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imax = index.get_loc(maxval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imin = index.get_loc(minval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imax = index.get_loc(maxval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imin = index.get_loc(minval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imax = index.get_loc(maxval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:562: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imin = index.get_loc(minval, method=\"nearest\")\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/missing.py:563: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  imax = index.get_loc(maxval, method=\"nearest\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c9c146b20>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###interpolate GEOS-chem to higher resolution from modis\n",
    "\n",
    "interp_AOD550_2014_2019_wet = AOD550_2014_2019_wet.interp(lat=MODIS550_2014_2019_wet[\"lat\"], lon=MODIS550_2014_2019_wet[\"lon\"])\n",
    "interp_AOD550_2014_2019_dry = AOD550_2014_2019_dry.interp(lat=MODIS550_2014_2019_dry[\"lat\"], lon=MODIS550_2014_2019_dry[\"lon\"])\n",
    "\n",
    "interp_AOD550_2014_2019_wet['AOD550nm_total'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9c9c1e5c70>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(interp_AOD550_2014_2019_dry['AOD550nm_total']-MODIS550_2014_2019_dry).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hdT9zkiRJYmtra3R9F9mRn9SBzpTRYazzdchjIPa8ul3jbNXC9+/fx19//TXaMn3WoO3F8pEkyWjlm+uc3/gA1EVXrSMydUwWmLVbap5llb+8gOve3t6VV3a7GFQra5LhxXvtqm2ni/d+ZpUBLc/lf0wK0kZcfxXV8Wf9xefdKpPTqty+Vt8s16qT08Z/gyJ1Qt4zNaIefdwmmTWJr2idUrfklFUmglUlOeWqsnvnKv12yWnVoPwr/2maxu3bt3PfX0UC36xY7SqTCq+rKXHPKvdF5tGEGEFT7qWI+sVXIxY/6aoJfaomlKs8Ym/1uL6S0/5R5Xq6SWPydSgXV7WoZLBJbYp2uz3aUaPT6cSPP/448xpMih9cp09Q5TIyrkntvSqp81hnRvt5te3n8d//4cOH8e233869KOZ40sy469x/F63T+Nx1zqMu9Xad2k6rnuvSpHrTc74ZcRIAkJzGwowP6C+qAzmtkRtR/QH5SQ3c604STtM0/u///m8hgb7xznlExF9//RXv3r2b2Tmf1JEZ78i9evUqt+OZNwEl4nr3zaSOfavVih9//HEhq+1M6kxe91qOS9M0tra2Rp2ERQZhMnmrLy+yw1j36zCNgdiPmnaNL656ddF4+RgPRi0qYJg3WbDdbserV69KDUhOC+itcmfSNE3jw4cPMRgM4vj4eGLwJLOM8jfpGs2q64oYHwwqY9Dm7t27kSRJtNvtODk5udY5Twuir+JZ6Ln80aSyPM+qq/Mafy6PB2pXlZxW9fb1tGOoby6r2k7Y8yanjT8PrzrwNu2ZuohdyJltVlJC0Ql54ztpTbKq+mnSYF5mkZMOi6pKcspVZHXjVX8/yWmrp/wr/5lpkwdWEXeeFQtf5XW7jqbEPevaFxlX9xhBU+6lOsdXj4+PIyJGEz6TJIl3797ljvlkLvb9xq9l3ftUdS9X04i91e/6pmk6Guf94YcfZpbNiH92y7m448miZGPR8+wm0el04t69e9HtdqPdbheKV1W9nm7SmHzdysW8JiWmXSeRL28XlaL3T/Z7Z676u1S9jIx/ZhPae1VT97FO7efy28+zni3jC9VfdcezvHGqshekXufxuYj61tt1bTste65LU+pNz/lmxUkA4H9XfQI0x+vXr0d/XlS2/ebm5tSO2GAwmCuwvgplBPgi/ml8X3R2dhaPHz8u9Fntdjt2dnbiyZMn8eTJkzg4OIhXr17F27dvY2NjY+7P2d/fHwUifvnll9zGcZIkU6/XVVfPyxro443rTqcTv/zyy8Ia1r1e79IEmX6/X/g3z/PgwYNRZ7Hb7S68sxjxzzbXZWnCdWC6Jl7jVqsVz549m9mpHw6Ho0Bkr9eLk5OThQQKnz17NjHYenF1o3XSarWi3W5Hr9eLN2/erHSiQ9mDNRExqn8jPtblV1lVK8/jx48jSZJotVrXDjZH/NMGmXTvb29vL3SFXubT7/cnJqa9evVqYWVn/Lm8v78/CrCuQh3a19OOob5pjiywn5WPqzxf8+677PNdk/LlJWxkivbJ6njNPnz4sOpTqJ00TUeTy5axSArlUP6V/8y0WMYqfqNZx6zrdWtC3LPOfZFME2IETbiX6h5f3dnZGY0pHRwcxMnJSbx9+7bwb5Zdy7r3qZpQrshXx+vbarVGz8e3b9/OjA9mZXBzc7O0RTKysehpCfbZ5OxXr17Fzs5OdDqdlSemRRiTn6SO5WIeeQmU8+xulmdnZ2diGfzzzz8Lfc6rV6/i4OAg9vb2rjxmVocykmlCe69q6j7Wqf38Udnt5/Ed8C7KEns6nU68efPmyr973hjZ/v5+qc/7dR6fq3O9Xde207LnujSh3vSc/6gpcRIAiJCcxgKdnp5GRCx8ZbV79+5NfX88Ka4qJgV4Op3OwiYuZSskXHR2draQCcPzds4jPnbA+v3+KIA1rXM+bUWQq0qSJP7v//7v3GdP6xBfR6fTubRi8tnZ2ZW2Eh93enp6rpNV5gS3bFBn0ZpwHZiu6dd42mpcSZLEgwcPIuJjQGfRZfTg4GBiuZz0m6+jVQ0QbW9vT1zF7Mcff1z4QH2v1xt9z36/f+WB4XH9fn90/os8ZwGt6jg8PJw4wFfGPTre9jw6OlrIPVpU3dvXGfVN/WUDbBsbGwuZwDHpM8aTXyjPtOSUVqtVeLXWOiYMqLeL++677yJN09jY2Fib3SKaSPlX/jNfffVV7nuraBvNmsRTx+vWhLhnE/oiTYgRNOFeanJ8dWdnZ+76syl9qiaUK/I15frOmlRd5i4lF7Xb7Xj48OGl17vd7rUmute9nq7TmHxTysVFw+Fw4u7F3W732u2BSeNbw+EwDg8P5/6MrI+6s7Nzpd+8TmWkCe29qqn7WKf280fLaj9PejYdHh6OEtMWsdv9pOdFRMS//vWva3/2LOs2PtfUertObaeIcue6NKHe9Jz/qClxEgDISE5jIdI0HQUXFh3InrUL26zVjpft8PBw4spDeZ2zq9rc3JzY8D86OlpYZ2+ea5kFa7///vuZHYNbt25Nff/mzZvzn1z8s3rGxe9bRicl0+12L3UeB4PBtVbTGG/8d7vd0lYnzHz//fcL/bymXAfyrcM1zit3aZrG7u5upGk6MVixKHm/ZXb8dbaKwaFsBa2L8gLHi7CzszNa5WsRKwpmdcvDhw8Xfs6dTmdiG8H9ujynp6fx4sWLS68/ffq0tHt0c3NzdN2XfZ2b1L5W39RbNqje7Xbj2bNnC/nMvD5Xv9836bBEg8Fg6mDjo0ePCn9mXa9XXc97FQaDwag++ve//73is+GqlP9/1PW8F+nOnTu5763i92niYgF1j3s2pS/ShBhB3e+ldYivzjOe1KQ+VRPKFfmadH2v0r4ty8W2V7aLx1U1pZ6OqP6YfESzysW4vISIRUwm73Q6Exe2efHixVIWgKtbGal7e69q6j7Wqf380TLbz2dnZ+fiEoPBIF68eBHtdnthz41OpzPx/hsOh6U/F9dtfK6p9XamDm2niHLnutS93vSc/6hJcRIAyEhOYyHGE8Rm7XRWVLfbndroHA6HlRm4Hw6HEycKP3r0qJSGc17Df1GdvVkdl+Pj49GK4bOSCCM+BjunrSz+zTffFDq/LEAwbm9vr/REhp2dnUvf4+zs7EqJkhcnRS1j5fVF727YhOvAdOtwjWfVM2UM1lw8ft5KYUVXUeR6Tk9PJ95jeUHWRcomOSdJcq1VVw8PDyNN02i1WvHkyZNFnd45eQOzg8Fg5mr/XE+SJLkruc7THruObNBymUHLprWv1Tf1labpaEA97ze8qrz65fnz5ws9Dv+Ytjpgu92OnZ2dhR/zxo0bC//MeXzyyScrOW4TZc+Ag4OD0gZHKZ/yz7hZcbhlD9a/e/duqccrW93jnk3pizQhRlD3eylCfDWiWX2qJpQr8jXt+k6LaS97PO3ipN/rxMCaUk9nqj4m37Rykbm440im3W4vrI7OK4OLWKRwmrqVkSa096qkCWOd2s+raT+PX7OyYqHzHLsM6zQ+19R6e1zV205la0K96TnfrDgJAIyTnMZCvH79OiJmJ5Jd1f3796e+f3p6uvBjXsWk4Fqr1SplYktE/mpbg8FgIavKzLqWP/zwQ0REoc7s06dPL51ztjJekU7M8fHxpe9Y5m990aQtkPf39wsnSv7666/n/ntZAdBF/U5NuQ7kW5drPGuy4N7eXukTULvdbm7Q6MWLF2s96WBZk3/TNM1dJXPRwaBJWq3WKEiUta2u4qeffoqI2e2n6xg/14umTfjl+ra3tye+Pul5WYZFrBhbRNPa1+qb+sqebWUkpeRdj//85z8LPQ4fDYfDqff5Mup86ufw8DCSJFnKJB7Ko/wzybTJG8tMFkuSZK5kuDqtblv3uGdT+iJNiBHU/V5al/jqrOvSpD5VE8oV+Zp2fVut1mgXm4uWnZz24cOH0Z97vd61Jp42pZ7OVHlMPqJ55WLW5y5ygnfeZyVJUuouQXUrI3Vv71VJE8Y6tZ8/WkX7Ofvd+/1+JEkSvV5v4Ukvs45dlnUan2tqvX3x+NOsuu108TMWre71puf8R02KkwDAOMlpXFuSJKMOUlkTdL7++uup71dhp6S8RuqjR49KPW7eCh+LWFVmWuf89PR0tMpIkU5O1nF78+ZNPH36NE5OTuLt27eFAhrD4XBiZ7bs33pcu92eeL/nTRjPs6pG/yICSE26Dky2Ttd4Wkc/7xzKMC3pY1ErcNbRsnYaePDgwcSJfr1er7SAXpIksb+/H3fv3o0vvvhi1Ka56s6ww+Fw9B0+/fTTOD4+Li2JP68NMhgMajVhsk4eP3488b4o8x69qNvtlr5iWaaJ7Wv1TT0lSRL9fr+0a5RXpsaf6SxO3uSMiOtNjhufZEezJEkyWm08byVw6kH5Z5Jpdfsyk/arsvjaItU57tmUvkhTYgR1vpfWKb46TZP6VE0pV0zW1Ov77bff5r63zDbIeNvqOhNPm1JPj6vqmHxEc8vFtDGYeXZYmVer1cq9bmXtElTHMlLn9l7V1H2sU/v5o1W1n7OFc7JyW8aCldPuwzLjMOsyPtfUevuiKredipzrVdW53vSc/6hJcRIAuEhyGtf28uXL0Z8XGagbN2tr4PEEuVXIGoyTlL2qw7179ya+vojO3rTOefZ9pw1oTNNut2Nzc/NKHZe8yUTLXr180vGSJCm03frFYNyydvxqt9vXXnWjSdeByVzjj/JWFS1Dp9PJPd60uqbplrFz2unpaW5boqx7PkmSuHv37mj1uYuuskLczz//PPrz0dFRHB0dxe7ubty+fXvhK85Nq8PLXt1uHQ2Hwzg7O5v43rJX91xGPdDU9vU06pvqygZiy7r3ZvV1WZxp9X273V7aLpTL9Omnn059X1LNbNmkgOuu7M9qKf+XKf8fbW5u5tbFv/3229LOY94+VJ2uW13jnk3qizQlRlDXeyli/eKreb9Xk/pUTSlXTNbU6zttnD3bVWIZsvq12+1eOUGiSfX0uKqOyUc0t1yMf6+LFp3Ac/PmzYmvl/Esr2sZqXN7r0qaMNap/fzRKtvPR0dHo13Tyiofecdf5g7245o0PtfUevuiKredLiqjHNW53vSc/6hJcRIAuEhyGteWTZItu7M2qxE6LYBYtryG6TJWWmq1WrnHKWtSaZIkMRgMotPpLH1i1mAwmBhQKzMwkidvMCfbIn0eFwOxy+wAfPXVV1f+t027DlzmGv/jzp07SzlOZlqQbNIKQixG3iB8u90ura6bFdj6448/Cn9mXj2Spmlsb28vvJ7J+22WOYlzXeSV/+tM5riqZexmuG7t6wj1TZVlg2jLHhiJMECwaNN2TTo5OVnimVAX/X4/hsNhtFqtRiYvrRPln2nyJgHkLQ6xaGmaxmAwaNyq/XWNezapL9KUGEFd7yXx1X9k928T+lRNKVdM1uTrm1f+rrKrzVVk7Z2I603AbFI9PY9VjsmPn8MkdS8X09r6i463f/nll7nvLXryfl3LSF3be1VT97FO7ed/rLL9nB27zITWvOt5lbHxRWjS+FxT6+15VaHttAx1rTc95//RpDgJAFwkOY1rGQ9Yf/PNN6Uea9aubKtKREnTNDd4WdZOcvMep6xAePZ9VxGoy+uIL+u3vmhSUub4NvdF9fv9pW2ffJ3AetOvA67xuGUnfXQ6ndzAS5qmcXp6utTzWQd5QbCIcoNBv//++9T3rxIU+vPPP6e+v+j759atWxNfF9BarOFwmDtIvYrnctnPxXVsX0eob6qu7MGBvGvhebo4+/v7uX2tp0+fXrsMLqsfx/KkaTrqF33//fcrPhuuQ/lnlmkTIJaxo+zz589H59FkdYh7Nq0v0tQYQR3upQjx1Yua0qdqarnioyZf32ll8OXLlws/3kVZe2da8sosTaun57HKMflMU8tF3ueVMTF62mf++uuvCztOk8pIXdp7VdKEsU7t5/NW1X6OmL7r6iLkLYK5qjZuk8bnmlpvz6sKbadVqEu96Tl/XlPiJABwkeQ0riULIl0nkDyvWav5pGmaG2wpUxZMn2RZq93mNfyTJCm18/H111+X9tmTTAuorWpl4byVzvJWpbpoUkdg2j21SJ9++umV/l0TrwPnucard//+/dz37GazeNN+0zIDQh8+fFj4Z86q9xe9Stknn3wy8fVZgW+KmXaP3rt3b4lnshzr3L5eNvXN/Mq+9/IGY9+/f1/qcdfFYDDInQSzt7e3soEvqu27776LNE2j2+26R2pM+WdeeUmoy5iM8NNPP0Wr1ZrrflzGLsaLUse4Z9P6Ik2JEdTxXhJfvawpfaqmlCsma/L1bbVaEydfRixnAdjsGNNiUbM0rZ4uYtlj8uOaWC6mfacy2tvTPnORk4PrXEbq2N6rmrqPdWo/X7aq9nNE+Yk9ec/6v/76q9TjVklZ43NNrLevYpVtp2WoY73pOX9ZU+IkAHCR5DSu5fXr1xGxvEmxs4Imy1jJ9qIXL15MfL3dbi9tdZW8lUgiIt69e1fKMVut1tK3wM6bDLLKrbjzOgrzrqQx6dq9ePFiKTtVZPdn0SB7E68D57nGqzdtN9IkSVaSjN1Uw+Ew9/dst9ulrJKZmVZ/R+QHw6Yp83wn+fzzzye+brWlxUmSJHfXtGkr69XZuravV0F9M7+yBwiaWJarZHd3d+LrvV4vdnZ2FnKMql5Dg0xXMxgMRqusPn36dMVnw3Uo/8xrc3Nz4oTtae3xRchWNn706FFpx1iVOsY9m9YXaUqMoI73kvjq/MdflGXd700pV0zW9OubFwe67g4dswwGg9FE6WmxqFmaVk/PaxVj8hePv0zLKBfTPmvZO6ctMhGjzmWkju29KmnCWKf28/zHX5Rp98WdO3dKPXZTkkKvo6zxuSbW20Wtuu20DHWsNz3n5z/+olR13AKA5pOcxpWNB5KXtcrwrCS4LFluWaZNTLh58+bSzqPVauU2KMuaUDoryLRoaZrm/t6r3I671WrlBnPnuR/zOhq7u7ulJ1tmHbwinZGmXgf+4RpXw6yBojInxq2bVdblsybDXmUlw1nldNGrhOUFHZu0s9SqTbtHl90eW4Z1bl+vgvpmPmUP4EfkP0/XaaXQsjx+/HhivbSxsREHBwcrOKNqqfPEmzLt7+9HxMedtQzg1ZfyP53yf9mzZ88mto2yZ8KipWka+/v70Wq15k6WrNMzqW5xzyb2RZoSI6jbvSS+elmT+lRNKVdM1vTr2+12c58DZT5PX758OfP4szSxnp7XqmPATSwX03Z6WvbOaYvcdSpPHcpI3dp7VVP3sU7t58tW2X6OWN0uRot6JtZBWeNzTay3i1p122kZ6lZves5f1qQ4CQBcJDmNK8tWW2i1WkvrmM461rTGbBl+/fXX3PeWtfpUJq9ztegtuTPLDGJGTO94X2VnmUXK+y2Gw+HMDve0YNz+/n5sbW2VtqJMu92Ot2/fxqtXr+b+N029DvzDNa6Oac/ZJibkrcq037Lse77b7U6cFNtqteLk5ORKwahvv/02999tbGwsbUEBFmfaCmerGhwq0zq3r1dFfTPbsvs+LM7x8fFo96txGxsb8ezZs6WeyzoNrNfd4eFhJEkS7XZ7YTtrsXzKP1c1abfEJElKWS33wYMHERHx/fffL/yzq6Bucc8m9kWaEiOo270kvjr/ceuoKeWKydbh+uY9UweDQSnP0jRNR+3yqyzGlmliPT2vVT9Dm1gulpEsNu9nLioZru5lpG7tvaqp+1in9vP8x12GZT8z1lkZ43NNrLeLWnXbaRnqVm96zs9/XABoAslpXFm20sL9+/eXetxZgets9bVl+M9//pP7Xt7W1WX55JNPJr7+559/lnK8ZXcOfv7559z3Vr2K1XVWs2m1WlPv6eFwGHfv3o3t7e1SEi+L/nZNvQ78wzWujmnP2Sbt2rNqV1mpcZF6vV68ffs2Dg4OYm9vL54+fRpv37698rFbrVa8evUqer3eqMy22+3Y29tb+iRcFmPa8y1bmaxJ1rl9vSrqm9k+/fTTVZ8CVzAYDOLo6OjS66tITKE+kiSJFy9eRMTkBBXqQfnnOjqdTpycnFx6/ejoaKFto+Pj4xgOh9Hr9eaeGLTquExRdYt7NrEv0pQYQd3uJfHVy5rUp2pKuWKydbi+056nZYyxZ/MJWq3WtSZDN7GenteqJ+w2sVxMq4/LmIQ87TMXlQRS9zJSt/Ze1dR9rFP7+bJVtp9X/ZuvkzLG55pYbxe16rbTMtSt3vScv6xJcRIAuOj/rfoEqKfxHRwWveXzLLMC15NWRS5DmqZTO4PLXk0mr9Fa1ioOy/5+0wKqq165Z1qHYTAYzLxn9/b2Zm6rPRgMYjAYRKvViq+++iq+/vrr6Ha7S++kNfk68JFrXB2zkk4Gg0Ejd01aplnBpGUl/swKHhbVbrfj4OBg4kqF1Muse3TVz+VFW/f29aqob2bLm1BBdSVJEru7u5deLzMxZVErXS/aX3/9NfX9VQ/8VU123/R6vUYmga8D5f8fyv/VdbvdePXqVWxtbZ17/cGDB/Hq1atrt0sPDw/jxYsX0el0CvXb6tj+r0vcs8l9kabECOpyL0WIr07StD5VU8oVkzX9+rZardjY2Jg4nv7TTz/FkydPFnq8RSx22+R6eh6rrjuyc2hSuZj2my5757RF1JFNKSN1au9VSRPGOrWfL1tl+7mqca4mKmt8rmn1dlGrfm4sS53qTc/5y5oWJwGAcZLTuJLxFQ0uDtJXwenpaelJCu/evZv6/rIbz3mN1jICqBHL/X5JkixlRbGrmnb8169fz+zwt1qtODk5ie3t7ZnHStM0zs7ORoNG7XY7bt68GXfu3Ilut1vqb9H064BrXDW3bt2a+v6vv/669skC1/Xrr7/mvrfOA1lUx7rdo+vevl4V9c1sy15dmOvb3t6+1K4te8ekJj6X102/34/hcBitViv29vZWfTpckfLPonQ6nXj16lU8ePBgdE+laRpbW1vx448/XnmC3+PHj+Ps7Cw6nU78+OOP596bNVmzjhPE6hL31BepvrrcS+Krk+lTQbV88803E5PT0jRd6Bj7YDCIJElGx7yqda+nV113NFW73R7dn+OWvXPaInZ3aUoZqUt7r2rqPo6k/TzZKtvPq/7N14nxuXKsyz1cl3rTc34ycRIAmkxyGleyrN3Jrqrf75eenDYpWDmuLitQXdUyA1lX3a58Wab9FmmaRpqmM3+vbrcbT58+nbi69zRJkkSSJOfKZKfTia+++mrUiVyUdbgO6841rpZZ32XWKvjMNq0ur+OkP5rn999/z31v1YHaMqx7+3pV1Dc0zePHjy89T3q9XukLGWg71FuapnF0dBQREd9//32j+hXrRPln0TqdTvzyyy9xdHQ0Wok4S1Db29uLnZ2duT9rMBjE/v5+JEkySnwrqq59gDrEPfVF6qEO95L4KlAH2QTUSfXfIsfYX758ee54V7Xu9bTncjk2NjbixYsXE99LkmSh99Vvv/2W+94iyluTykgd2ntVU/exTu1n1pnxuXKsU5msQ73pOQ8A60dyGoXN2hK4CgaDQemNw1nBsy+++KK0Y6+baQHbKnQAZgX1skkns2xubsbJyUns7u5eawBjOBzGcDgcBdS73W70er1rB7fX5TqsM9e4elqtVu7zYNZgE7NNC+hW4Z5flTRNRwHJ8f99+PBhdD+O/5ny1H1QsSjt69VR39AUx8fHlxbTWUZiSsTstkMV6826JjmU4bvvvos0TaPb7Za+2BHlUP6LUf7n12q14uDgIHq9Xuzu7o7aRkdHR3F0dBQbGxvxzTffxK1bty7dC8PhMAaDQfT7/dG/m5bUVrVJpYtU9binvki+qsUIqn4via8yj6qVKxarLte31+uNFugYt6gx9mx3huxY1/2sada5nq6LKpaLb775ZmnJaXmL0C1q4nfTykjV23tVU/exTu1n1l0Vx+eqWG+Tr+r1puc8AKwfyWkUdnp6GhEfV0z48ccfV3Ye33333dQd3F6/fn3tYPc0szqBVWmYztoGvA7qPiG2SEel2+3GL7/8Eru7uzEYDBZy/MFgEIPBINrtduzt7V2547hO12FducbVc+PGjdwA0ocPH5Z8Ns2zbok/edI0HdUVg8Fg5rMgCxJmg7N1f3ZU2br9ttrXq6O+oQn6/f6liXXLSkyJqG7b4f3797nvVWHgrwoGg8EovrSs+6UM2SSBZWu32ytPmFH+J1P+F6vT6cSbN29iMBjEy5cvR8+Ns7OzqTHqzMbGRjx58mRqeZnV7qpKe/iq6hz3rMpvv4y+SB1iBHW+l6quifHVKqhDueLq6np985LTIiKeP38eT548udbnv379OiI+ft/rTmhVT9dPHcpFu92Obrc7sS1xenq60MSxvO+zqLksTSwjVW7vVU3dxzrr3s7Rfua6qjA+V4d6m+mqXG/W/f7wnAeA4iSnUUjWIYmIuHfv3konMnzzzTdTB/77/X6pyWmzJni8evWqtGOv2rKve9W3Kp/1exQNGLRarTg5OYnhcBhHR0cL6zgmSRK7u7ujbb2LXsd1uw7ryDWunllbyHM90+6ZTz75ZIlnsnzD4TD6/f7UAHOr1Yqvvvoq7ty5M5p0fHEi5enpaezu7i7jlLmgiffoOrevV019Q90NBoPY398/99oyE1Mi6pnoUYcJKsuQ3Tt7e3srT7K6jpcvX+au+l6mabtALYPyfzXK/9V1u93RRNXT09MYDAbx/v37+PPPPyNJktFOI+12Oz777LO4c+fO3DHqWe2uJkx4rmrcc937InWMEVT1XhJfJVPHcsX8mnB9W61WbGxsTBxr/+mnn66dnHZ8fBwREffv37/W50Ssdz1dp75GHctFr9eb2IZYVLti2me12+2FJUc1tYxUtb1XNXUf69R+Zt2tanyujvX2POr+TL+OqtabnvMAsH4kp1FIv98f/bnMxK95dLvdqdtbD4fDSJKktIlFVW88Ux1XDRh0Op04OTmJJEni5cuX8dNPPy0k+DAYDGJraytOTk5qPfGuKBOrm6+J1/izzz6L4XA48b26rzBUBdPumU8//XR5J7JE/X4/jo+Pc++fTqcTvV4vut3uWtURddTEe1T7enXUN9TZcDiM7e3tc68tOzFlHqsawJr2bFXXRxweHo5iR6tMsOJqlP/plP/ybW5uLnS1/Wntrlar1agJNlWLe65rX6QJMYKq3Ut118T46rI1oVyRr2nXN28h2Gyx2qvuHJWN0WfHuK51rafros7lYnNzMzqdzqW4aJIkMRwOF7JLxvj8mnGL7Lc2vYxo7023jmOdVaL9zHUte3yuzvU281FvLpbnPAAUJzmNQl6/fh0RHxuyVRgMv3fvXm5AL+LjCh1lTS6yMsLyrPtv3W6348mTJ/HkyZNIkiQGg0H8+uuv8Z///OfKnaAkSeLu3bvx6tWruQPr634d1oFrXD1NH1BieY6Pj+OHH37IrTc2Njbi22+/XchgK1yVemh11DfUVZqm8eDBg3OvPXz48NorvF9Vu93OHdRd1QBW3VdPLlOSJKOdxqqWzMRsyv9syn/9TJt01IRd0yYR91yNJsYI3EusWhPLFf9o6vWdthDsy5cvr5yclo3dL2pSs2drNTWlXDx9+jTu3r176fWjo6M4OTm51mcPh8OJu5ZsbGxcuXxNsi5lpCrtPRZrXe5fyLOs8bmm1NvMryr1puc8AKwfyWnMLVshKmL1u6Zler3e1OS0fr9fWnLajRs3SvlcLqv6bz2r07bIRM52ux29Xm9UBrMOZBbcLrpyztbW1tydRteh+Vzj6pkWqGni962SpuwUNBwOY3d3N/f7PHz4ML799lv3Uw29f/9+1aewcFWvh5pMfUMdpWkaW1tb59qIq0xMiZienLKq5/a0NvTnn3++xDOpnv39/YhY/KQwyqf8z0f5r59p/dB1eE6Je5ZvXWIE7qV86xhfLdu6lKt1tQ7X99GjR3F0dHTp9bOzs0jT9ErfLRu7X9R8gqo/W9dN08pFu92Og4ODUYwgk7UdrjNJf1LZ6nQ68ezZsyt/5iTrWEZW2d6rkzqMdVb9/tV+pmxlj881rd7masRJ8nnOA8DiSU5jbqenp6M/37t3b4Vn8o9sB7e8hmKSJJEkSSnbCU9bYdiWvsuz7r911oHMpGka7969G612krf9+7gHDx7E27dvr3Ue634d1oFrvBrTfveqB3HqrgkrOB0eHo52A7mo3W7H06dPGzfQRr1pX6+O+oY6evDgwbkBs1UnpkRE3Lx5c+KK2BGr26FwWpumqbvwzOP09HR0rb799tul1jPTjpWmaaFzWdeBUeV/Psp//bx79y73vXVITrtomXHPdeiLrHOMQAydsqxzuVoH63J9e73exASaiKstApslprVardjc3Lz2+UWsRz1dF00tF71eL4bD4aVFkXd3d+PVq1dX6nsfHx9f6iO2Wq348ccfr3OqEykj1WnvVU3dxzrX5f5lvZU5PtfUepvrq0q96TkPAM0kOY25ZcG4brdbqckv9+/fz+1MRUS8fPmylAkqZSS8MVnVf+tZQb1lnn+r1YputzuasJKmaTx//jx++umn3E5dmqbx+PHjmau0uQ7NV/XfaB2v8bTv3MTvu2zTEtzrHgh7/PhxnJ2dTXxvY2Nj4StzUo5pO3CsapJzmTzXVkd9Q908fvz43OBYFRJTIiK+/PLL3PequHPSOpfv8QliW1tbKzyT84qey9OnT0cTPp88eVKJclA25X9+yn/95F2zVqtlslCsd9zzusQIzlvne2kd46tlUa6abZ2ub/ZMnLTQwnWS0+7fv7+Q84vwbKqKdSoXmSRJ4sGDB/Hq1atC/+709PRS0me73Y6Tk5NS5tkoI5eV2d6rmrqPdVb9/tV+pmxljc+tY73N1YmT5Kv6+QNAFf3vqk+AehgOh6OJseMrJ1TB119/PfX9n376qZTjfv7551Pfr0Ogpy6q3tCfda2nnX/RVdGLarVa8eTJk/jll19iY2Mj9++dnZ3NXPmkydeBj6r+G63jNa7rZMLhcBjHx8fndl2tomm7BdR5NcHt7e3cYHOv1xNsrpFp5bzO92ge7evVUd9QJ4eHh+fquaokpkRMLy9VSyputVqVWngI5qH8L4byX03T4nJfffXVEs9kceoU92xyX6QJMYI63UtV7j9FrGd8tQxNKFfkW8frm5eAliRJ7u7AeX8/e05+8803Czm3iGbX03XR9HIxGAyi3+9Hq9WKg4ODc/2l4XAYd+/enWsHkYiP/dbd3d1zr21sbMSbN29Kq2ebUEbq1N6rmrqPdVa9/an9TNnKGJ9rer1NverNqj8nPecBYPEkpzGXn3/+efTnbEXmquh0OjOTf8oIIM1qfObtckFxVf+tZx1/2vlvbW3F7du3Sw8Kt1qtePbsWRwcHOT+nWw1wzxNvg585BpXS52DIIPBII6Ojs61H6poVvuhjh4/fpw7YWBjY2NqPUD1NPEenabq9VBTqW+ok+Pj43M7p+/t7VUmMSUipu5qs4pn2LRjTpu4AlWk/Bej/NfPtInfsxZnqypxz9VrSozAvbQ46xZfLUNTyhWTrev17Xa7uYsXvHz5cu7POT4+jojZY/dFVf3Z2nTrUC729/cjIuL777+PXq93aSJ2kiSxtbUV29vb0e/3I0mSUbskS+I8PDyM27dvn+u3ttvtePr0aelJAE0oI3Vq71VN3ceRqn7/aj9TpjLG59ah3qZe9abnPACsn/+36hOgHrLdx6athrBKvV4vjo6Oct/v9/sL72BlWxnnqcMqRHUx67dOkmSlnYH379/nvjdtclLEP52cwWCwlMTPbOfDLMg+blY5afJ14CPXuFpmrUg663ox26z7Jk3TWu0o0O/3c1dBa7fbVkGroW63mxvQXXWgtgza16uhvqEuTk9Pz/X79/b2cld3X6VWqzVxQLBqySk3b95c4plUz8HBwcoG4re2tnIXUSpzJfU6U/6LU/7r57fffst9r2qLxc2rSXHPOvZFmhQjaNK9JL5ab00qV1y27tf3/v3755JqMmdnZ3PHyV+/fh0REY8ePVrouTWxnq6LdSgXp6enkSRJdDqdUTsjm4idJEkcHx+PYvSDwWCu3QTb7Xbs7OyM2hRla0IZqVN7r2rqPtap/cw6W/T43DrU23xUp3rTcx4A1o+d05hpMBiMJlZUdZXWWQ3tLBi+aNMa0E2cMLwqrVar0is+TbvWX3311VyfMU8ge1F6vV5uMPz09DT3363DdVh3rnG1TJuYFiEQsgj37t2b+v67d++WdCbXl6bpxGBgpk4DafxjVrB21c/lMmhfL5/6hjoYDoexu7s7+u9lJKYMh8M4PDwsvFrytHbpsp/b056bX3755RLPBK5O+b8a5b9+/vOf/0x8vaqLxRVRh7hnRLP6Ik2NEdThXhJfba6mlis+cn0jvvnmm9z35mkXn56ejhIgypgg26R6ui7WpVxk9/eker/dbsfBwUH897//jZOTk3j48GF0u91ot9ujZJ9WqxWdTme0G82bN2/izZs3S0tMyzSljNShvVc1dR/r1H5mnS1yfG5d6m3Oq0O96TkPAOtHchozjTcgq7pKa7vdntopS9O0lAb5tCDfr7/+uvDjrbNpEzFmddjL9vvvv+e+N29C57KDggcHBxNXyJr1Wzb9OuAaV0nexLQIiQKLMisQVqe6fNoOst1ut9I7H6VpuvKgY1XNukfrNKg8L+3r5VPfUHVJksSDBw9G/72sHZMGg0G8ePGi8LPnzp07ue8t+7mdtztXhF0RqQfl/+qU/3oZDoe5faJvv/12yWezeHWJezapL9LUGEFd7iXx1WZqarniI9f341h73vecJzkt+zuzkiSuqkn1dF2sQ7kYn0Ny69atqX+32+3GkydP4uTkJN68eRNv376N//73v/H27dt49epVPHv2LHq93sp2/mhKGalLe69KmjDWqf3Mulrk+Nw61NtcVpd603MeANaL5DRmynYdq/oqrbOC3S9fvlz4Macl603rRFLctAk+0zoKy5A3wShbKW0e0yYNleXRo0eXXps1Warp1wHXuEqmPReWveJik01r36z6np9XmqZTJwgsYwLvVQ0Gg7h9+3ZsbW2t+lQqa9o9uszV0JZF+3r51DdUWZqmsb29PRqYXFZiSsQ/A2JFdxiaNsi77IHCvPZzp9OZOIAIVaL8X4/yXy8///zzxNc7nU4j4h11iXs2pS/S5BhBXe4l8dXmaXK5wvUdlxcHSpJk6jM4SZJRnLKs36sp9XRdrEu5GI+vryqpbFGaUkbq0t6rmrqPdWo/s64WNT63LvU2l9Wl3vScB4D1IjmNqU5PT0eTQL755psVn810szpmZ2dnCz9mu93ODVRa2WOxpk3wWeXE7GnHLrq987K/x6Qy89dff039N+twHdada1wNs37rslYfXUfTAmF1SfzJFhKYZNqKt4vw4cOH0j6bj6bdo6teSawM2tfLpb6h6h48eDAaGFpmYkrEPwNiRScnTXuO/fHHH9c+ryLyBtWa2H6meZT/61H+6yUvbj1pskdd1SHu2ZS+SNNjBHW4l8RXm6fp5Wrdub7/2NzczF3I4Pnz57n/LlsgttPplJbg05R6ui7WpVyMx9frvohHk8pIHdp7VVP3sU7tZ9bRIsfn1qXeZrI61Jue8wCwXiSnMVW2amur1ar0Fs8R853j6enpwo87bXLMKhvQaZo2bnWRhw8f5r63itVAZh23aEJnGffnNK1W60qDRE2/DrjGVTBtt8+NjY3aD5JVSbfbnfp7ruqeT9M07t69G48fP575d6fVH2W335Y9yXYddbvd3Pp6VSuelj3QoH29POobqmx7e3tUDz98+HDpq3tmiR1X6TPlrVa8zOd2mqa5ySlff/310s4DrkL5vx7l/+qSJInj4+M4Pj5eWrtzMBhMvF7tdnvq7gd1U5e4ZxP6Ik2PEdTlXhJfbZaml6t15/qed//+/Ymvn52d5Sa3/PTTTxFRfmJ9E+rpuliXcjHeDq/bTlmTNKWM1KW9VyVNGOvUfmbdLHJ8bl3qbSarS73pOQ8A60NyGlNlq7bWZcX8WQP207axvqper5fbKcyS+1ah3+9HmqZx48aNlZ3Don377be57717926JZ/KPvEBup9Mp3MmftppNWW7evHnuvz/55JOZ/6bp1wHX+KJlryaYpunU3T6fPHmykONMqx+u+53fv38fERGffvrptT5nWfb29nLfK6PtMI9+vx9Jksy1OuO0cll2+ajb6pF1lTeoPG3icZnKPua6tq/VN8XVrb5hfvv7+6P238OHDxd2P85r/DnX6XQK//u8QatlDrDltZ/b7faVvhMsi/J/fcr/1Wxvb8fdu3fj6Ogojo6OYnt7O7a2tkpvo+VNRprWT62jusQ9m9AXaXqMoC73kvhqszS9XK071/e8aZMwJz2DT09PR+2lshPrm1BP18W6lIvxYzUhOa0pZaQu7b2qqftYp/Yzq1T38bl1qbeZrC71puc8AKwPyWnkGl9ZYdKWvFU0K4luMBiU0qnMWwlu2ipyZfvhhx8iovyO5jK1Wq3clTSOj4+XfDYfAwZ5HZWrTCBJ03Tpq4FcXM1knom9Tb8OuMYXlb1D0EXTgke9Xm9hq/dNW23rut85C0LWZfBm2oDdKgdsIuYb0J/W1ih716NVBQrXzbR7dNmroUUsZxW8dWxfq2+Kq1t9w3wODw9H9WCv11t6YkrEPwNSV61H2+12bhlaVp8vr/287B2ooAjlfzGU/+LGkyLHDYfD2N3dLe24SZJMnIzU6XQatWtaRH3inhH174s0PUZQl3tJfLVZml6u1p3re1673c6tbyY9v8bb78tQ93q6LtalXIzHFFe1Y8eiNaGM1KW9VzV1H+vUfmaV6j4+ty71NpPVpd70nAeA9SE5jVxZoKDVatVmVd1WqzUzqFXGihE7OzuVCvSMJ+HVJbFwXnkraSRJsvTOVt69dJ0VNJZ9v1xcxWbeIEfTrwOu8bhlf99pgZdFTia0k815VVpRcDgcjlbpnHWPz1rNc1HJJXmaMmBbB3n36Cramv/5z39KP8Y6tq/VN8XVsb5hun6/Hy9evIiIj2Xt4OBgJefx66+/RkTErVu3rvwZeeVoWStV5w2qzVrUB1ZF+V8c5b+4ae3LshY7i/iYkDlJUydA1CXuWee+yLrECOpyL4mvNsO6lKt15fpOllffXHx+jU/oXNZCCHWup+tincrFeEwxS6Cqu6aUkbq096qmrmOdGe1nVqXO43PrVG+Try71puc8AKwHyWlMNB5Mvn///orPpphZQa2yGuR5gZ5VBDKzjuzGxkbpq6As27SVNJ4/f77Uc8kLGHz//fdX/sx+v7/UVcuyCb2ZeTtYTb8OuMbjlvkcPz4+zg0gLnIXm4hyd7L5/fffIyJqk9weMX1FwWWv1JSVsW63e+1rPi0p5Lpm7dg1T322qpU66yjvHk2SJHcCchmWGRxet/a1+qa4OtY35BsMBrG/vx8RH8vaqhJTIv5Jwr1OWcgrSz/99NOVP3Ne45M/xj18+LBxMQKaQflfHOW/uFmTeCLyE/6ue9xJu6b1er3GToCoS9wzorl9kabECOpyL4mvroemlCsmW9fru7m5OVdyS/bnTqez1ESOptbTddGkcjEeU0zTNHfxiLppQhmpS3uvauo+1qn9zKo0dXwuoln1NvnqUm96zgPAepCcxkTjDb47d+6s8EyKm7Ul/HA4LKVB3uv1YmNj49Lryw5kDgaD0YSJJ0+eLO24y/TkyZOJE1DPzs6W1tk6PT2dGDDY29u79uTYZa5oMr7zSdFdEpt+HXCNM2mazgysLeo4eYHPTqdTygTNvCDRdRNPsmt2nd0GVuHHH3+c+HqSJEt7No9PUJxnZbRZAep5Jlpe1SJ3fliXgfvryrtHlzmouMxjrVv7Wn1TXF3rGy4bDoexvb0dER8nqzx79myl55K1da/b3pw0GShN09KTfPPaLXmrUsIqKf+LpfyXo4z+yu7u7qXX2u32SpMzl6Eucc+69kXWKUZQl3tJfLX+1qlcrSPXN1/e4rWTktOWvWNYXevpulincnFxd+kXL14sdSG4sjSljNSlvVc1dRzrHKf9zCrUeXxuneptpqtLvek5DwDNJzmNS9I0PbeSbx23rJ8UbBtXVoP82bNnE3+vFy9elNrhG5etNF3GiipV8uOPP07snE6a2FGGo6OjS691u93CwbVJfvjhh6V0uJIkOXeciwH4eTT5Oly0riv2rNM1LnoeZRxj0n3WarXi6dOnpRzz5s2bE1//7bffrvyZ2UBTq9VaWBBxWeWv0+nkrii5v7+/lLo8q8e73e7cK0xNq+/LOufhcBhnZ2fR6XRyA3TzHDvbNemzzz4rfA7r+FzOu0fHB3nLNBwOZ7ajr7sT1kXr1r5W38yvzvUN5yVJEg8ePIiIj/XfKhNTIs4v1nPdMr+5uTnxM8pcATJN04nP6r29PQO8VI7yv1jK/9XMc60XnQjf7/cnJiqW1RarkjrFPevaF1mXGEGd7iXx1fpbl3J10br0kdf1+s7yzTff5L7X7/djMBiMznHZyWkR9a2n62JdysWkydTb29tL32GqDE0oI3Vq71VJXcc6x2k/swp1Hp9bl3q7rpbVr6pTvek5DwDNJjmNS7777rtzjcg6Dj5MC5hHlLtaxMnJycTXs1Wgy5QFkxa1ym7eta/CPZHXOR8MBqUHjB8/fnypE73Iybxpmi4l8HHxd7pKJ6vJ12FeVSgPZXKNPx4zSZJRIL0Mp6enuXXT06dPSxvYzKsvx1c6KipbVayugzc7Ozu5AyVl3gMRH3+7LNmiSD2el/QRUV6bJ6un9vb2coPF7969m/k5f/zxR0REfPrppws7t6Y/l/Pu0bLvz4gYTR6ftmJYGYMcTWpfT6O+Kabu9c1Fi07srIs0TWN7ezvSNI1ut5tb3ss8/nA4jMFgEP1+Px4/fjxa1TdiMckIk54dZ2dnpQ0KTyrj7Xa7coNqVY43lG3ad1yH759R/hdP+b+6acl7i07uGw6HE9t7BwcHa7Eyb53inhH17IusS4ygTvfSOsVXV/ksL7NPtS7lal5Na7O6vpO12+3ctkm/34+XL19GxGoS0zJ1rKfzVK2NvE7lYlISz9HRUdy+fTv29/ej3+9Hv9+P09PTc//LFowbDAYxHA5jOBxGkiSjCdtVeFbWvYzUqb1XNXUc6xyn/bwc6zomMUndx+fWqd7OVK3tVAV1qjc955fDcx6AVZGcxjn9fv/cRIyIWMouDIs2a/WdJEkmrk67CO12e2KDNUmSUgN9x8fHo07lohrMVW+kdrvdiQGto6Oj0q7vpDLSarVyV/W4znHK+g4Rl1fTvs6qfk2+DuPyykPVy8kirMs1zvP06dNotVqjwadFGw6HuSsA7e3tXWlFuXnlDTCnaXrl7/r69euIWOygdF45KyuQkxcAHgwGcXh4WMoxx++Dvb29Qs/kJ0+e5L43voLtomQr42YrHt65c2fi38uCydP8/vvvETG77TbJOj+XJ92jSZLE48ePSzvm1tZWpGk6ddXNiHLKZZPa19Oob4opo77Js4yBg7xjvH//vvRjr9KDBw9G9dRgMIgvvvhiqf+7fft2bG1txfb2duzv709sf15Xt9uNhw8fXnq9jIHuvMHHqu3EM61MrUM9Pu07rsP3zyj/i6X8X89XX3116bVsEuUiJyaO7xY47uHDhyud2L1sdYp71rEvsk4xgjrdS+seX617n2qdytW4dYm9rev1ncejR48mvp7tlBGx2uS0OtbTeapWrtapXOTt7JS1A/b392N/fz92d3fP/W97e3v0v62trdja2oq7d+/G3bt34/bt23H79u1L/c/9/f04Pj5e2vybJpSROrX3qqZuY50XaT/Xu/08S94xVpXEUffxuXWqtzNVaztNs8y5LnWqNz3nm/2cB2C9SU5jJAuuXbSsbX8XaZ6OVZmrRWxubk5ciWowGJQyabjf74++zyJX2Z123cvszBTR6/UmBjUfPHiw8HM8PT29VEba7Xa8evVqoSsbZx2e8YlaizY+Eabdbk+dZD6PJl6Hi9Z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      "text/plain": [
       "<Figure size 4200x3600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure S3 in paper\n",
    "titles=['Wet season: GEOS-Chem', 'Wet season: MODIS', 'Wet season difference',\n",
    "        'Dry season:GEOS-Chem','Dry season: MODIS','Dry season difference']\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2,3, figsize=[14, 12], dpi=300, subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "axes = axes.flatten()\n",
    "\n",
    "\n",
    "fig.suptitle(\"Aerosol optical depth 2014-2019 seasonal averages comparison\")\n",
    "\n",
    "\n",
    "(interp_AOD550_2014_2019_wet['AOD550nm_total']).plot(ax=axes[0], cmap='Reds',vmin=0, vmax=1,add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "(MODIS550_2014_2019_wet).plot(ax=axes[1], cmap='Reds',vmin=0, vmax=1,add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "(interp_AOD550_2014_2019_wet['AOD550nm_total']-MODIS550_2014_2019_wet).plot(ax=axes[2], cmap='RdBu_r',vmin=-1, vmax=1,add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "\n",
    "(interp_AOD550_2014_2019_dry['AOD550nm_total']).plot(ax=axes[3],  cmap='Reds',vmin=0, vmax=1,add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "(MODIS550_2014_2019_dry).plot(ax=axes[4], cmap='Reds',vmin=0, vmax=1, add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "(interp_AOD550_2014_2019_dry['AOD550nm_total']-MODIS550_2014_2019_dry).plot(ax=axes[5], cmap='RdBu_r',vmin=-1, vmax=1,add_colorbar=True,cbar_kwargs={'shrink': 0.8, 'label': 'AOD (550 nm)'})\n",
    "\n",
    "for i in range(0,6):\n",
    "    #plotting GEOS-chem results\n",
    "    axes[i].add_feature(cartopy.feature.LAND, color='0.98')\n",
    "    axes[i].add_feature(cartopy.feature.OCEAN, alpha=0.5)\n",
    "    axes[i].coastlines()\n",
    "    axes[i].add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=.5)\n",
    "    axes[i].set_extent([-84, -33, -53, 13])\n",
    "    axes[i].set_title(titles[i])\n",
    "    \n",
    "    \n",
    "plt.show()\n",
    "fig.savefig('../PP2_figs/AmazonHealth_FigureS3.png', dpi=fig.dpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AOD from AERONET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1993-06-12 00:00:00+00:00</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-15.917000</td>\n",
       "      <td>-47.900002</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.074334</td>\n",
       "      <td>0.089929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111731</td>\n",
       "      <td>0.595244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.510178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.675353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1993-06-16 00:00:00+00:00</td>\n",
       "      <td>Cuiaba</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-15.555244</td>\n",
       "      <td>-56.070214</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.088421</td>\n",
       "      <td>0.095267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117581</td>\n",
       "      <td>0.424234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.497630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.924333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1993-06-17 00:00:00+00:00</td>\n",
       "      <td>Cuiaba</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-15.555244</td>\n",
       "      <td>-56.070214</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.099877</td>\n",
       "      <td>0.110915</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.144628</td>\n",
       "      <td>0.547807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.628609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1993-06-18 00:00:00+00:00</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-15.917000</td>\n",
       "      <td>-47.900002</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.212684</td>\n",
       "      <td>0.228017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.260287</td>\n",
       "      <td>0.296380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.309144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.430179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1993-06-19 00:00:00+00:00</td>\n",
       "      <td>Cuiaba</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-15.555244</td>\n",
       "      <td>-56.070214</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.081171</td>\n",
       "      <td>0.084164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099885</td>\n",
       "      <td>0.313678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.405796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.655140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                   datetime      Name Andes?   season   Latitude  \\\n",
       "0           0  1993-06-12 00:00:00+00:00  Brasilia      N  dry_wet -15.917000   \n",
       "1           1  1993-06-16 00:00:00+00:00    Cuiaba      N  dry_wet -15.555244   \n",
       "2           2  1993-06-17 00:00:00+00:00    Cuiaba      N  dry_wet -15.555244   \n",
       "3           3  1993-06-18 00:00:00+00:00  Brasilia      N  dry_wet -15.917000   \n",
       "4           4  1993-06-19 00:00:00+00:00    Cuiaba      N  dry_wet -15.555244   \n",
       "\n",
       "   Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  AOD_500nm  \\\n",
       "0 -47.900002        1100.0            9.0   0.074334   0.089929        NaN   \n",
       "1 -56.070214         234.0            3.0   0.088421   0.095267        NaN   \n",
       "2 -56.070214         234.0            3.0   0.099877   0.110915        NaN   \n",
       "3 -47.900002        1100.0            9.0   0.212684   0.228017        NaN   \n",
       "4 -56.070214         234.0            3.0   0.081171   0.084164        NaN   \n",
       "\n",
       "   AOD_440nm  440-870_Angstrom_Exponent  380-500_Angstrom_Exponent  \\\n",
       "0   0.111731                   0.595244                        NaN   \n",
       "1   0.117581                   0.424234                        NaN   \n",
       "2   0.144628                   0.547807                        NaN   \n",
       "3   0.260287                   0.296380                        NaN   \n",
       "4   0.099885                   0.313678                        NaN   \n",
       "\n",
       "   440-675_Angstrom_Exponent  500-870_Angstrom_Exponent  \\\n",
       "0                   0.510178                        NaN   \n",
       "1                   0.497630                        NaN   \n",
       "2                   0.628609                        NaN   \n",
       "3                   0.309144                        NaN   \n",
       "4                   0.405796                        NaN   \n",
       "\n",
       "   340-440_Angstrom_Exponent  \n",
       "0                   0.675353  \n",
       "1                   0.924333  \n",
       "2                   0.988320  \n",
       "3                   0.430179  \n",
       "4                   0.655140  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##now can add the aeronet means\n",
    "daily_ARNET_df= pd.read_csv('../AQ_Amazon_files/daily_AERONET_means.csv')\n",
    "monthly_ARNET_df=pd.read_csv('../AQ_Amazon_files/monthly_AERONET_means.csv')\n",
    "daily_ARNET_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4018</th>\n",
       "      <td>4018</td>\n",
       "      <td>2021-11-30 00:00:00+00:00</td>\n",
       "      <td>Punta_Arenas_UMAG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-53.135400</td>\n",
       "      <td>-70.884500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.045723</td>\n",
       "      <td>0.048234</td>\n",
       "      <td>0.056119</td>\n",
       "      <td>0.058443</td>\n",
       "      <td>0.437277</td>\n",
       "      <td>0.151676</td>\n",
       "      <td>0.520030</td>\n",
       "      <td>0.435624</td>\n",
       "      <td>0.036622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>4019</td>\n",
       "      <td>2021-11-30 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.076487</td>\n",
       "      <td>0.103768</td>\n",
       "      <td>0.165558</td>\n",
       "      <td>0.199756</td>\n",
       "      <td>1.413242</td>\n",
       "      <td>1.524597</td>\n",
       "      <td>1.539613</td>\n",
       "      <td>1.381811</td>\n",
       "      <td>1.244338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>4020</td>\n",
       "      <td>2021-12-31 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.018902</td>\n",
       "      <td>0.020952</td>\n",
       "      <td>0.026917</td>\n",
       "      <td>0.033314</td>\n",
       "      <td>0.926608</td>\n",
       "      <td>0.629667</td>\n",
       "      <td>1.202255</td>\n",
       "      <td>0.741339</td>\n",
       "      <td>-0.533245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>4021</td>\n",
       "      <td>2021-12-31 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.071953</td>\n",
       "      <td>0.098177</td>\n",
       "      <td>0.158535</td>\n",
       "      <td>0.192538</td>\n",
       "      <td>1.420939</td>\n",
       "      <td>1.587308</td>\n",
       "      <td>1.565650</td>\n",
       "      <td>1.382836</td>\n",
       "      <td>1.328283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>4022</td>\n",
       "      <td>2022-01-31 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.018200</td>\n",
       "      <td>0.019736</td>\n",
       "      <td>0.025493</td>\n",
       "      <td>0.031836</td>\n",
       "      <td>0.836393</td>\n",
       "      <td>0.637538</td>\n",
       "      <td>1.143862</td>\n",
       "      <td>0.633961</td>\n",
       "      <td>-0.528232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>4023</td>\n",
       "      <td>2022-01-31 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.111798</td>\n",
       "      <td>0.148398</td>\n",
       "      <td>0.209221</td>\n",
       "      <td>0.235761</td>\n",
       "      <td>1.283426</td>\n",
       "      <td>1.310844</td>\n",
       "      <td>1.368162</td>\n",
       "      <td>1.284635</td>\n",
       "      <td>1.078179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4024</th>\n",
       "      <td>4024</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.019888</td>\n",
       "      <td>0.020804</td>\n",
       "      <td>0.025685</td>\n",
       "      <td>0.031816</td>\n",
       "      <td>0.812853</td>\n",
       "      <td>0.790364</td>\n",
       "      <td>1.203563</td>\n",
       "      <td>0.552298</td>\n",
       "      <td>-0.683922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4025</th>\n",
       "      <td>4025</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.114358</td>\n",
       "      <td>0.183114</td>\n",
       "      <td>0.217191</td>\n",
       "      <td>1.445593</td>\n",
       "      <td>1.408980</td>\n",
       "      <td>1.510809</td>\n",
       "      <td>1.449293</td>\n",
       "      <td>1.140956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026</th>\n",
       "      <td>4026</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>0.085947</td>\n",
       "      <td>0.124275</td>\n",
       "      <td>0.202389</td>\n",
       "      <td>0.242326</td>\n",
       "      <td>1.523836</td>\n",
       "      <td>1.444994</td>\n",
       "      <td>1.590111</td>\n",
       "      <td>1.512251</td>\n",
       "      <td>1.176150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4027</th>\n",
       "      <td>4027</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.020506</td>\n",
       "      <td>0.022174</td>\n",
       "      <td>0.026260</td>\n",
       "      <td>0.030746</td>\n",
       "      <td>0.709865</td>\n",
       "      <td>0.440183</td>\n",
       "      <td>0.908389</td>\n",
       "      <td>0.575420</td>\n",
       "      <td>-0.820154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                   datetime               Name Andes?  \\\n",
       "4018        4018  2021-11-30 00:00:00+00:00  Punta_Arenas_UMAG      N   \n",
       "4019        4019  2021-11-30 00:00:00+00:00            SP-EACH      N   \n",
       "4020        4020  2021-12-31 00:00:00+00:00          CEILAP-RG      N   \n",
       "4021        4021  2021-12-31 00:00:00+00:00            SP-EACH      N   \n",
       "4022        4022  2022-01-31 00:00:00+00:00          CEILAP-RG      N   \n",
       "4023        4023  2022-01-31 00:00:00+00:00            SP-EACH      N   \n",
       "4024        4024  2022-02-28 00:00:00+00:00          CEILAP-RG      N   \n",
       "4025        4025  2022-02-28 00:00:00+00:00            SP-EACH      N   \n",
       "4026        4026  2022-02-28 00:00:00+00:00          Sao_Paulo      N   \n",
       "4027        4027  2022-03-31 00:00:00+00:00          CEILAP-RG      N   \n",
       "\n",
       "       season   Latitude  Longitude  Elevation(m)  instrument_ID  AOD_870nm  \\\n",
       "4018  dry_wet -53.135400 -70.884500          25.0          234.0   0.045723   \n",
       "4019  dry_wet -23.481630 -46.499670         754.0          828.0   0.076487   \n",
       "4020  dry_wet -51.600503 -69.319250          19.0          737.0   0.018902   \n",
       "4021  dry_wet -23.481630 -46.499670         754.0          828.0   0.071953   \n",
       "4022  dry_wet -51.600503 -69.319250          19.0          737.0   0.018200   \n",
       "4023  dry_wet -23.481630 -46.499670         754.0          828.0   0.111798   \n",
       "4024  dry_wet -51.600503 -69.319250          19.0          737.0   0.019888   \n",
       "4025  dry_wet -23.481630 -46.499670         754.0          828.0   0.080296   \n",
       "4026  dry_wet -23.561500 -46.734983         786.0          669.0   0.085947   \n",
       "4027  dry_wet -51.600503 -69.319250          19.0          737.0   0.020506   \n",
       "\n",
       "      AOD_675nm  AOD_500nm  AOD_440nm  440-870_Angstrom_Exponent  \\\n",
       "4018   0.048234   0.056119   0.058443                   0.437277   \n",
       "4019   0.103768   0.165558   0.199756                   1.413242   \n",
       "4020   0.020952   0.026917   0.033314                   0.926608   \n",
       "4021   0.098177   0.158535   0.192538                   1.420939   \n",
       "4022   0.019736   0.025493   0.031836                   0.836393   \n",
       "4023   0.148398   0.209221   0.235761                   1.283426   \n",
       "4024   0.020804   0.025685   0.031816                   0.812853   \n",
       "4025   0.114358   0.183114   0.217191                   1.445593   \n",
       "4026   0.124275   0.202389   0.242326                   1.523836   \n",
       "4027   0.022174   0.026260   0.030746                   0.709865   \n",
       "\n",
       "      380-500_Angstrom_Exponent  440-675_Angstrom_Exponent  \\\n",
       "4018                   0.151676                   0.520030   \n",
       "4019                   1.524597                   1.539613   \n",
       "4020                   0.629667                   1.202255   \n",
       "4021                   1.587308                   1.565650   \n",
       "4022                   0.637538                   1.143862   \n",
       "4023                   1.310844                   1.368162   \n",
       "4024                   0.790364                   1.203563   \n",
       "4025                   1.408980                   1.510809   \n",
       "4026                   1.444994                   1.590111   \n",
       "4027                   0.440183                   0.908389   \n",
       "\n",
       "      500-870_Angstrom_Exponent  340-440_Angstrom_Exponent  \n",
       "4018                   0.435624                   0.036622  \n",
       "4019                   1.381811                   1.244338  \n",
       "4020                   0.741339                  -0.533245  \n",
       "4021                   1.382836                   1.328283  \n",
       "4022                   0.633961                  -0.528232  \n",
       "4023                   1.284635                   1.078179  \n",
       "4024                   0.552298                  -0.683922  \n",
       "4025                   1.449293                   1.140956  \n",
       "4026                   1.512251                   1.176150  \n",
       "4027                   0.575420                  -0.820154  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_ARNET_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38415.760000</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>0.096772</td>\n",
       "      <td>0.105693</td>\n",
       "      <td>0.131874</td>\n",
       "      <td>0.141456</td>\n",
       "      <td>0.748648</td>\n",
       "      <td>1.076203</td>\n",
       "      <td>0.862144</td>\n",
       "      <td>0.745716</td>\n",
       "      <td>1.073749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38326.451613</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>146.193548</td>\n",
       "      <td>0.051088</td>\n",
       "      <td>0.060352</td>\n",
       "      <td>0.088903</td>\n",
       "      <td>0.100688</td>\n",
       "      <td>1.075572</td>\n",
       "      <td>1.394296</td>\n",
       "      <td>1.281236</td>\n",
       "      <td>1.054423</td>\n",
       "      <td>0.955923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Arica</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38635.764706</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>0.088816</td>\n",
       "      <td>0.113412</td>\n",
       "      <td>0.173679</td>\n",
       "      <td>0.198567</td>\n",
       "      <td>1.200480</td>\n",
       "      <td>1.126055</td>\n",
       "      <td>1.312316</td>\n",
       "      <td>1.208292</td>\n",
       "      <td>1.018363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38381.090909</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.051307</td>\n",
       "      <td>0.061756</td>\n",
       "      <td>0.089549</td>\n",
       "      <td>0.105166</td>\n",
       "      <td>1.008382</td>\n",
       "      <td>1.297607</td>\n",
       "      <td>1.178971</td>\n",
       "      <td>0.954158</td>\n",
       "      <td>0.876374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-Bariloche</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38632.166667</td>\n",
       "      <td>-41.146944</td>\n",
       "      <td>-71.163333</td>\n",
       "      <td>840.00</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>0.036142</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>0.046554</td>\n",
       "      <td>0.051609</td>\n",
       "      <td>0.717304</td>\n",
       "      <td>1.116963</td>\n",
       "      <td>0.867249</td>\n",
       "      <td>0.635957</td>\n",
       "      <td>0.471605</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime              Name Andes?   season    Unnamed: 0   Latitude  \\\n",
       "0 2015-01-15    ARM_Manacapuru      N  dry_wet  38415.760000  -3.212972   \n",
       "1 2015-01-15     Alta_Floresta      N  dry_wet  38326.451613  -9.871339   \n",
       "2 2015-01-15             Arica      N  dry_wet  38635.764706 -18.471667   \n",
       "3 2015-01-15         CEILAP-BA      N  dry_wet  38381.090909 -34.555425   \n",
       "4 2015-01-15  CEILAP-Bariloche      Y  dry_wet  38632.166667 -41.146944   \n",
       "\n",
       "   Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  AOD_500nm  \\\n",
       "0 -60.598056         49.99     628.000000   0.096772   0.105693   0.131874   \n",
       "1 -56.104453        277.00     146.193548   0.051088   0.060352   0.088903   \n",
       "2 -70.313333         25.00     528.000000   0.088816   0.113412   0.173679   \n",
       "3 -58.506411         26.00     800.000000   0.051307   0.061756   0.089549   \n",
       "4 -71.163333        840.00     781.000000   0.036142   0.039193   0.046554   \n",
       "\n",
       "   AOD_440nm  440-870_Angstrom_Exponent  380-500_Angstrom_Exponent  \\\n",
       "0   0.141456                   0.748648                   1.076203   \n",
       "1   0.100688                   1.075572                   1.394296   \n",
       "2   0.198567                   1.200480                   1.126055   \n",
       "3   0.105166                   1.008382                   1.297607   \n",
       "4   0.051609                   0.717304                   1.116963   \n",
       "\n",
       "   440-675_Angstrom_Exponent  500-870_Angstrom_Exponent  \\\n",
       "0                   0.862144                   0.745716   \n",
       "1                   1.281236                   1.054423   \n",
       "2                   1.312316                   1.208292   \n",
       "3                   1.178971                   0.954158   \n",
       "4                   0.867249                   0.635957   \n",
       "\n",
       "   340-440_Angstrom_Exponent  \n",
       "0                   1.073749  \n",
       "1                   0.955923  \n",
       "2                   1.018363  \n",
       "3                   0.876374  \n",
       "4                   0.471605  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARNET_2014_wet, ARNET_2014_dry= AERONET_seasonal_means(daily_ARNET_df, 2014)\n",
    "ARNET_2015_wet, ARNET_2015_dry= AERONET_seasonal_means(daily_ARNET_df, 2015)\n",
    "ARNET_2016_wet, ARNET_2016_dry= AERONET_seasonal_means(daily_ARNET_df, 2016)\n",
    "ARNET_2017_wet, ARNET_2017_dry= AERONET_seasonal_means(daily_ARNET_df, 2017)\n",
    "ARNET_2018_wet, ARNET_2018_dry= AERONET_seasonal_means(daily_ARNET_df, 2018)\n",
    "ARNET_2019_wet, ARNET_2019_dry= AERONET_seasonal_means(daily_ARNET_df, 2019)\n",
    "ARNET_2015_wet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34677.763158</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>0.047845</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>0.078915</td>\n",
       "      <td>0.084504</td>\n",
       "      <td>1.033394</td>\n",
       "      <td>1.023971</td>\n",
       "      <td>1.253966</td>\n",
       "      <td>1.062875</td>\n",
       "      <td>1.071058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34894.078947</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>123.421053</td>\n",
       "      <td>0.047886</td>\n",
       "      <td>0.058219</td>\n",
       "      <td>0.081334</td>\n",
       "      <td>0.092140</td>\n",
       "      <td>1.018854</td>\n",
       "      <td>1.492322</td>\n",
       "      <td>1.112283</td>\n",
       "      <td>1.004868</td>\n",
       "      <td>1.113672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Arica</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34621.353659</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>0.128627</td>\n",
       "      <td>0.150368</td>\n",
       "      <td>0.219307</td>\n",
       "      <td>0.252282</td>\n",
       "      <td>0.964301</td>\n",
       "      <td>1.186351</td>\n",
       "      <td>1.167869</td>\n",
       "      <td>0.918335</td>\n",
       "      <td>1.147524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34617.555556</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>0.013896</td>\n",
       "      <td>0.015536</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>0.027831</td>\n",
       "      <td>1.080644</td>\n",
       "      <td>1.700547</td>\n",
       "      <td>1.484956</td>\n",
       "      <td>0.835009</td>\n",
       "      <td>0.931400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34619.987654</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.057373</td>\n",
       "      <td>0.072412</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>0.126352</td>\n",
       "      <td>1.085850</td>\n",
       "      <td>1.269262</td>\n",
       "      <td>1.236505</td>\n",
       "      <td>1.037081</td>\n",
       "      <td>0.766168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime            Name Andes?   season    Unnamed: 0   Latitude  \\\n",
       "0 2014-01-15  ARM_Manacapuru      N  dry_wet  34677.763158  -3.212972   \n",
       "1 2014-01-15   Alta_Floresta      N  dry_wet  34894.078947  -9.871339   \n",
       "2 2014-01-15           Arica      N  dry_wet  34621.353659 -18.471667   \n",
       "3 2014-01-15          CASLEO      Y  dry_wet  34617.555556 -31.798815   \n",
       "4 2014-01-15       CEILAP-BA      N  dry_wet  34619.987654 -34.555425   \n",
       "\n",
       "   Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  AOD_500nm  \\\n",
       "0 -60.598056         49.99     620.000000   0.047845   0.053477   0.078915   \n",
       "1 -56.104453        277.00     123.421053   0.047886   0.058219   0.081334   \n",
       "2 -70.313333         25.00     528.000000   0.128627   0.150368   0.219307   \n",
       "3 -69.295685       2485.00     781.000000   0.013896   0.015536   0.021907   \n",
       "4 -58.506411         26.00     800.000000   0.057373   0.072412   0.107979   \n",
       "\n",
       "   AOD_440nm  440-870_Angstrom_Exponent  380-500_Angstrom_Exponent  \\\n",
       "0   0.084504                   1.033394                   1.023971   \n",
       "1   0.092140                   1.018854                   1.492322   \n",
       "2   0.252282                   0.964301                   1.186351   \n",
       "3   0.027831                   1.080644                   1.700547   \n",
       "4   0.126352                   1.085850                   1.269262   \n",
       "\n",
       "   440-675_Angstrom_Exponent  500-870_Angstrom_Exponent  \\\n",
       "0                   1.253966                   1.062875   \n",
       "1                   1.112283                   1.004868   \n",
       "2                   1.167869                   0.918335   \n",
       "3                   1.484956                   0.835009   \n",
       "4                   1.236505                   1.037081   \n",
       "\n",
       "   340-440_Angstrom_Exponent  \n",
       "0                   1.071058  \n",
       "1                   1.113672  \n",
       "2                   1.147524  \n",
       "3                   0.931400  \n",
       "4                   0.766168  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### concatanating the seasonal aeronet dataframes\n",
    "list_dfs=[]\n",
    "list_dfs =[ARNET_2014_wet, ARNET_2014_dry, ARNET_2015_wet, ARNET_2015_dry,ARNET_2016_wet, ARNET_2016_dry,\n",
    "          ARNET_2017_wet, ARNET_2017_dry,ARNET_2018_wet, ARNET_2018_dry,ARNET_2019_wet, ARNET_2019_dry]\n",
    "ARNET_conc_df =pd.concat(list_dfs, ignore_index=True)\n",
    "ARNET_conc_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38415.760000</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>0.096772</td>\n",
       "      <td>0.105693</td>\n",
       "      <td>0.131874</td>\n",
       "      <td>0.141456</td>\n",
       "      <td>0.748648</td>\n",
       "      <td>1.076203</td>\n",
       "      <td>0.862144</td>\n",
       "      <td>0.745716</td>\n",
       "      <td>1.073749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38326.451613</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>146.193548</td>\n",
       "      <td>0.051088</td>\n",
       "      <td>0.060352</td>\n",
       "      <td>0.088903</td>\n",
       "      <td>0.100688</td>\n",
       "      <td>1.075572</td>\n",
       "      <td>1.394296</td>\n",
       "      <td>1.281236</td>\n",
       "      <td>1.054423</td>\n",
       "      <td>0.955923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Arica</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38635.764706</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>0.088816</td>\n",
       "      <td>0.113412</td>\n",
       "      <td>0.173679</td>\n",
       "      <td>0.198567</td>\n",
       "      <td>1.200480</td>\n",
       "      <td>1.126055</td>\n",
       "      <td>1.312316</td>\n",
       "      <td>1.208292</td>\n",
       "      <td>1.018363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38381.090909</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.051307</td>\n",
       "      <td>0.061756</td>\n",
       "      <td>0.089549</td>\n",
       "      <td>0.105166</td>\n",
       "      <td>1.008382</td>\n",
       "      <td>1.297607</td>\n",
       "      <td>1.178971</td>\n",
       "      <td>0.954158</td>\n",
       "      <td>0.876374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-Bariloche</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38632.166667</td>\n",
       "      <td>-41.146944</td>\n",
       "      <td>-71.163333</td>\n",
       "      <td>840.00</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>0.036142</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>0.046554</td>\n",
       "      <td>0.051609</td>\n",
       "      <td>0.717304</td>\n",
       "      <td>1.116963</td>\n",
       "      <td>0.867249</td>\n",
       "      <td>0.635957</td>\n",
       "      <td>0.471605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-Comodoro</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38313.731707</td>\n",
       "      <td>-45.792153</td>\n",
       "      <td>-67.462875</td>\n",
       "      <td>49.00</td>\n",
       "      <td>782.000000</td>\n",
       "      <td>0.026752</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>0.032303</td>\n",
       "      <td>0.034709</td>\n",
       "      <td>0.558717</td>\n",
       "      <td>0.977936</td>\n",
       "      <td>0.858891</td>\n",
       "      <td>0.479787</td>\n",
       "      <td>-0.015704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-Neuquen</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38273.239583</td>\n",
       "      <td>-38.952220</td>\n",
       "      <td>-68.136660</td>\n",
       "      <td>271.00</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>0.047765</td>\n",
       "      <td>0.057757</td>\n",
       "      <td>0.084077</td>\n",
       "      <td>0.098991</td>\n",
       "      <td>0.962696</td>\n",
       "      <td>1.224097</td>\n",
       "      <td>1.154078</td>\n",
       "      <td>0.900427</td>\n",
       "      <td>0.449461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38306.257143</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.00</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>0.013727</td>\n",
       "      <td>0.015866</td>\n",
       "      <td>0.021100</td>\n",
       "      <td>0.021972</td>\n",
       "      <td>0.860257</td>\n",
       "      <td>0.952423</td>\n",
       "      <td>0.915541</td>\n",
       "      <td>0.929786</td>\n",
       "      <td>0.141853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>CUIABA-MIRANDA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38705.166667</td>\n",
       "      <td>-15.730910</td>\n",
       "      <td>-56.070860</td>\n",
       "      <td>210.00</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>0.029624</td>\n",
       "      <td>0.048855</td>\n",
       "      <td>0.075988</td>\n",
       "      <td>0.083370</td>\n",
       "      <td>1.749283</td>\n",
       "      <td>1.737920</td>\n",
       "      <td>1.355196</td>\n",
       "      <td>1.974155</td>\n",
       "      <td>1.667958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Huancayo-IGP</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38676.318182</td>\n",
       "      <td>-12.040200</td>\n",
       "      <td>-75.320900</td>\n",
       "      <td>3313.00</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>0.021699</td>\n",
       "      <td>0.027412</td>\n",
       "      <td>0.041918</td>\n",
       "      <td>0.049419</td>\n",
       "      <td>1.271228</td>\n",
       "      <td>1.843548</td>\n",
       "      <td>1.429962</td>\n",
       "      <td>1.242807</td>\n",
       "      <td>1.974458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Itajuba</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38392.381579</td>\n",
       "      <td>-22.413250</td>\n",
       "      <td>-45.452389</td>\n",
       "      <td>856.00</td>\n",
       "      <td>812.000000</td>\n",
       "      <td>0.045842</td>\n",
       "      <td>0.061303</td>\n",
       "      <td>0.095928</td>\n",
       "      <td>0.114035</td>\n",
       "      <td>1.382229</td>\n",
       "      <td>1.470841</td>\n",
       "      <td>1.495689</td>\n",
       "      <td>1.366508</td>\n",
       "      <td>1.243684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Ji_Parana_SE</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38458.437500</td>\n",
       "      <td>-10.934250</td>\n",
       "      <td>-61.851517</td>\n",
       "      <td>218.00</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>0.045481</td>\n",
       "      <td>0.053416</td>\n",
       "      <td>0.077395</td>\n",
       "      <td>0.089687</td>\n",
       "      <td>0.973418</td>\n",
       "      <td>1.332798</td>\n",
       "      <td>1.162465</td>\n",
       "      <td>0.947680</td>\n",
       "      <td>1.137693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>La_Paz</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38455.492754</td>\n",
       "      <td>-16.539000</td>\n",
       "      <td>-68.066467</td>\n",
       "      <td>3439.00</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>0.041932</td>\n",
       "      <td>0.049137</td>\n",
       "      <td>0.072726</td>\n",
       "      <td>0.086712</td>\n",
       "      <td>1.085998</td>\n",
       "      <td>1.345470</td>\n",
       "      <td>1.355662</td>\n",
       "      <td>0.997469</td>\n",
       "      <td>1.408567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Manaus_EMBRAPA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38385.500000</td>\n",
       "      <td>-2.890528</td>\n",
       "      <td>-59.969778</td>\n",
       "      <td>115.00</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>0.070334</td>\n",
       "      <td>0.077919</td>\n",
       "      <td>0.101807</td>\n",
       "      <td>0.109160</td>\n",
       "      <td>0.805857</td>\n",
       "      <td>1.034335</td>\n",
       "      <td>0.935663</td>\n",
       "      <td>0.789741</td>\n",
       "      <td>0.750583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Petrolina_SONDA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38406.047059</td>\n",
       "      <td>-9.069100</td>\n",
       "      <td>-40.320108</td>\n",
       "      <td>381.00</td>\n",
       "      <td>824.000000</td>\n",
       "      <td>0.074323</td>\n",
       "      <td>0.080085</td>\n",
       "      <td>0.114050</td>\n",
       "      <td>0.119550</td>\n",
       "      <td>0.783045</td>\n",
       "      <td>0.783403</td>\n",
       "      <td>1.019945</td>\n",
       "      <td>0.809909</td>\n",
       "      <td>1.150589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Rio_Branco</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38436.685714</td>\n",
       "      <td>-9.957467</td>\n",
       "      <td>-67.869350</td>\n",
       "      <td>212.00</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>0.041333</td>\n",
       "      <td>0.051318</td>\n",
       "      <td>0.074378</td>\n",
       "      <td>0.077124</td>\n",
       "      <td>1.055514</td>\n",
       "      <td>1.176443</td>\n",
       "      <td>1.081309</td>\n",
       "      <td>1.174184</td>\n",
       "      <td>1.475129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Santiago_Beauchef</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38325.638095</td>\n",
       "      <td>-33.457222</td>\n",
       "      <td>-70.661666</td>\n",
       "      <td>560.00</td>\n",
       "      <td>835.000000</td>\n",
       "      <td>0.099167</td>\n",
       "      <td>0.129473</td>\n",
       "      <td>0.186484</td>\n",
       "      <td>0.222424</td>\n",
       "      <td>1.163723</td>\n",
       "      <td>1.340854</td>\n",
       "      <td>1.259020</td>\n",
       "      <td>1.111635</td>\n",
       "      <td>1.102525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38629.088235</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>213.676471</td>\n",
       "      <td>0.099688</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.218198</td>\n",
       "      <td>0.250643</td>\n",
       "      <td>1.381193</td>\n",
       "      <td>1.241791</td>\n",
       "      <td>1.413410</td>\n",
       "      <td>1.409930</td>\n",
       "      <td>1.059937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Trelew</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38351.166667</td>\n",
       "      <td>-43.249806</td>\n",
       "      <td>-65.308611</td>\n",
       "      <td>15.00</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>0.039276</td>\n",
       "      <td>0.046566</td>\n",
       "      <td>0.056673</td>\n",
       "      <td>0.063327</td>\n",
       "      <td>0.426903</td>\n",
       "      <td>1.015125</td>\n",
       "      <td>0.378034</td>\n",
       "      <td>0.417497</td>\n",
       "      <td>0.924540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>UNC-Bogota</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38723.400000</td>\n",
       "      <td>4.636933</td>\n",
       "      <td>-74.080800</td>\n",
       "      <td>2594.00</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.138977</td>\n",
       "      <td>0.195948</td>\n",
       "      <td>0.302167</td>\n",
       "      <td>0.360059</td>\n",
       "      <td>1.267408</td>\n",
       "      <td>1.281887</td>\n",
       "      <td>1.373275</td>\n",
       "      <td>1.231905</td>\n",
       "      <td>0.915986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>UNC-Gaitan</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38400.666667</td>\n",
       "      <td>4.310680</td>\n",
       "      <td>-72.351800</td>\n",
       "      <td>192.00</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.216015</td>\n",
       "      <td>0.300538</td>\n",
       "      <td>0.472720</td>\n",
       "      <td>0.570669</td>\n",
       "      <td>1.432619</td>\n",
       "      <td>1.436573</td>\n",
       "      <td>1.495609</td>\n",
       "      <td>1.419862</td>\n",
       "      <td>1.332903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>UdeConcepcion-CEFOP</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>38372.717949</td>\n",
       "      <td>-36.842780</td>\n",
       "      <td>-73.025310</td>\n",
       "      <td>170.00</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.030437</td>\n",
       "      <td>0.042877</td>\n",
       "      <td>0.063217</td>\n",
       "      <td>0.074812</td>\n",
       "      <td>1.345874</td>\n",
       "      <td>1.000427</td>\n",
       "      <td>1.278605</td>\n",
       "      <td>1.366008</td>\n",
       "      <td>0.860340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime                 Name Andes?   season    Unnamed: 0   Latitude  \\\n",
       "40 2015-01-15       ARM_Manacapuru      N  dry_wet  38415.760000  -3.212972   \n",
       "41 2015-01-15        Alta_Floresta      N  dry_wet  38326.451613  -9.871339   \n",
       "42 2015-01-15                Arica      N  dry_wet  38635.764706 -18.471667   \n",
       "43 2015-01-15            CEILAP-BA      N  dry_wet  38381.090909 -34.555425   \n",
       "44 2015-01-15     CEILAP-Bariloche      Y  dry_wet  38632.166667 -41.146944   \n",
       "45 2015-01-15      CEILAP-Comodoro      N  dry_wet  38313.731707 -45.792153   \n",
       "46 2015-01-15       CEILAP-Neuquen      N  dry_wet  38273.239583 -38.952220   \n",
       "47 2015-01-15            CEILAP-RG      N  dry_wet  38306.257143 -51.600503   \n",
       "48 2015-01-15       CUIABA-MIRANDA      N  dry_wet  38705.166667 -15.730910   \n",
       "49 2015-01-15         Huancayo-IGP      Y  dry_wet  38676.318182 -12.040200   \n",
       "50 2015-01-15              Itajuba      N  dry_wet  38392.381579 -22.413250   \n",
       "51 2015-01-15         Ji_Parana_SE      N  dry_wet  38458.437500 -10.934250   \n",
       "52 2015-01-15               La_Paz      Y  dry_wet  38455.492754 -16.539000   \n",
       "53 2015-01-15       Manaus_EMBRAPA      N  dry_wet  38385.500000  -2.890528   \n",
       "54 2015-01-15      Petrolina_SONDA      N  dry_wet  38406.047059  -9.069100   \n",
       "55 2015-01-15           Rio_Branco      N  dry_wet  38436.685714  -9.957467   \n",
       "56 2015-01-15    Santiago_Beauchef      N  dry_wet  38325.638095 -33.457222   \n",
       "57 2015-01-15            Sao_Paulo      N  dry_wet  38629.088235 -23.561500   \n",
       "58 2015-01-15               Trelew      N  dry_wet  38351.166667 -43.249806   \n",
       "59 2015-01-15           UNC-Bogota      Y  dry_wet  38723.400000   4.636933   \n",
       "60 2015-01-15           UNC-Gaitan      N  dry_wet  38400.666667   4.310680   \n",
       "61 2015-01-15  UdeConcepcion-CEFOP      N  dry_wet  38372.717949 -36.842780   \n",
       "\n",
       "    Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  AOD_500nm  \\\n",
       "40 -60.598056         49.99     628.000000   0.096772   0.105693   0.131874   \n",
       "41 -56.104453        277.00     146.193548   0.051088   0.060352   0.088903   \n",
       "42 -70.313333         25.00     528.000000   0.088816   0.113412   0.173679   \n",
       "43 -58.506411         26.00     800.000000   0.051307   0.061756   0.089549   \n",
       "44 -71.163333        840.00     781.000000   0.036142   0.039193   0.046554   \n",
       "45 -67.462875         49.00     782.000000   0.026752   0.026743   0.032303   \n",
       "46 -68.136660        271.00     798.000000   0.047765   0.057757   0.084077   \n",
       "47 -69.319250         19.00     799.000000   0.013727   0.015866   0.021100   \n",
       "48 -56.070860        210.00     636.000000   0.029624   0.048855   0.075988   \n",
       "49 -75.320900       3313.00     890.000000   0.021699   0.027412   0.041918   \n",
       "50 -45.452389        856.00     812.000000   0.045842   0.061303   0.095928   \n",
       "51 -61.851517        218.00     580.000000   0.045481   0.053416   0.077395   \n",
       "52 -68.066467       3439.00     707.000000   0.041932   0.049137   0.072726   \n",
       "53 -59.969778        115.00     932.000000   0.070334   0.077919   0.101807   \n",
       "54 -40.320108        381.00     824.000000   0.074323   0.080085   0.114050   \n",
       "55 -67.869350        212.00     196.000000   0.041333   0.051318   0.074378   \n",
       "56 -70.661666        560.00     835.000000   0.099167   0.129473   0.186484   \n",
       "57 -46.734983        786.00     213.676471   0.099688   0.140202   0.218198   \n",
       "58 -65.308611         15.00     239.000000   0.039276   0.046566   0.056673   \n",
       "59 -74.080800       2594.00     305.000000   0.138977   0.195948   0.302167   \n",
       "60 -72.351800        192.00     305.000000   0.216015   0.300538   0.472720   \n",
       "61 -73.025310        170.00     108.000000   0.030437   0.042877   0.063217   \n",
       "\n",
       "    AOD_440nm  440-870_Angstrom_Exponent  380-500_Angstrom_Exponent  \\\n",
       "40   0.141456                   0.748648                   1.076203   \n",
       "41   0.100688                   1.075572                   1.394296   \n",
       "42   0.198567                   1.200480                   1.126055   \n",
       "43   0.105166                   1.008382                   1.297607   \n",
       "44   0.051609                   0.717304                   1.116963   \n",
       "45   0.034709                   0.558717                   0.977936   \n",
       "46   0.098991                   0.962696                   1.224097   \n",
       "47   0.021972                   0.860257                   0.952423   \n",
       "48   0.083370                   1.749283                   1.737920   \n",
       "49   0.049419                   1.271228                   1.843548   \n",
       "50   0.114035                   1.382229                   1.470841   \n",
       "51   0.089687                   0.973418                   1.332798   \n",
       "52   0.086712                   1.085998                   1.345470   \n",
       "53   0.109160                   0.805857                   1.034335   \n",
       "54   0.119550                   0.783045                   0.783403   \n",
       "55   0.077124                   1.055514                   1.176443   \n",
       "56   0.222424                   1.163723                   1.340854   \n",
       "57   0.250643                   1.381193                   1.241791   \n",
       "58   0.063327                   0.426903                   1.015125   \n",
       "59   0.360059                   1.267408                   1.281887   \n",
       "60   0.570669                   1.432619                   1.436573   \n",
       "61   0.074812                   1.345874                   1.000427   \n",
       "\n",
       "    440-675_Angstrom_Exponent  500-870_Angstrom_Exponent  \\\n",
       "40                   0.862144                   0.745716   \n",
       "41                   1.281236                   1.054423   \n",
       "42                   1.312316                   1.208292   \n",
       "43                   1.178971                   0.954158   \n",
       "44                   0.867249                   0.635957   \n",
       "45                   0.858891                   0.479787   \n",
       "46                   1.154078                   0.900427   \n",
       "47                   0.915541                   0.929786   \n",
       "48                   1.355196                   1.974155   \n",
       "49                   1.429962                   1.242807   \n",
       "50                   1.495689                   1.366508   \n",
       "51                   1.162465                   0.947680   \n",
       "52                   1.355662                   0.997469   \n",
       "53                   0.935663                   0.789741   \n",
       "54                   1.019945                   0.809909   \n",
       "55                   1.081309                   1.174184   \n",
       "56                   1.259020                   1.111635   \n",
       "57                   1.413410                   1.409930   \n",
       "58                   0.378034                   0.417497   \n",
       "59                   1.373275                   1.231905   \n",
       "60                   1.495609                   1.419862   \n",
       "61                   1.278605                   1.366008   \n",
       "\n",
       "    340-440_Angstrom_Exponent  \n",
       "40                   1.073749  \n",
       "41                   0.955923  \n",
       "42                   1.018363  \n",
       "43                   0.876374  \n",
       "44                   0.471605  \n",
       "45                  -0.015704  \n",
       "46                   0.449461  \n",
       "47                   0.141853  \n",
       "48                   1.667958  \n",
       "49                   1.974458  \n",
       "50                   1.243684  \n",
       "51                   1.137693  \n",
       "52                   1.408567  \n",
       "53                   0.750583  \n",
       "54                   1.150589  \n",
       "55                   1.475129  \n",
       "56                   1.102525  \n",
       "57                   1.059937  \n",
       "58                   0.924540  \n",
       "59                   0.915986  \n",
       "60                   1.332903  \n",
       "61                   0.860340  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = ['2014', '2015', '2016', '2017', \"2018\",'2019' ]\n",
    "ARNET_conc_df[ARNET_conc_df['datetime']=='2015-01-15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bounds = np.array([0.1,0.2,0.3,0.4, 0.5, 0.6, 0.7, 0.8,0.9,1.0])\n",
    "norm = matplotlib.colors.BoundaryNorm(boundaries=bounds, ncolors=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure NOT IN PAPER \n",
    "titles=['Wet season 2014','Dry season 2014','Wet season 2015','Dry season 2015',\n",
    "           'Wet season 2016','Dry season 2016','Wet season 2017','Dry season 2017',\n",
    "          'Wet season 2018','Dry season 2018','Wet season 2019','Dry season 2019',]\n",
    "\n",
    "AERONET_dfs=[ARNET_2014_wet,ARNET_2014_dry,ARNET_2015_wet,ARNET_2015_dry,\n",
    "            ARNET_2016_wet,ARNET_2016_dry,ARNET_2017_wet,ARNET_2017_dry,\n",
    "             ARNET_2018_wet,ARNET_2018_dry,ARNET_2019_wet,ARNET_2019_dry]\n",
    "\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(3,4, figsize=[14, 12], subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "axes = axes.flatten()\n",
    "\n",
    "\n",
    "fig.suptitle(\"GEOS-Chem vs. AERONET: AOD at 500nm in Amazon\")\n",
    "\n",
    "\n",
    "(AOD_2014_2019_dt[\"AOD500nm_total\"][1,:,:]).plot(ax=axes[1],  cmap='Oranges',vmin=0, vmax=1,add_colorbar=False)\n",
    "\n",
    "# Use for loop to save some coding. Useful when there are a lot of subplots.\n",
    "for i in range(0,12):\n",
    "    #plotting GEOS-chem results\n",
    "    (AOD_2014_2019_dt[\"AOD500nm_total\"][i,:,:]).plot(ax=axes[i],  cmap='Oranges',vmin=0, vmax=1,add_colorbar=False)\n",
    "     # Get all the data from the dataframe\n",
    "    lat, lon,site = AERONET_dfs[i]['Latitude'], AERONET_dfs[i]['Longitude'],AERONET_dfs[i][\"Name\"]\n",
    "    AOD_500 = AERONET_dfs[i]['AOD_500nm']\n",
    "    axes[i].scatter(lon, lat, transform=ccrs.PlateCarree(),\n",
    "           c=AOD_500, s=100, norm=norm,cmap='Oranges', edgecolor= 'dimgrey')\n",
    "    \n",
    "    axes[i].add_feature(cartopy.feature.LAND, color='0.98')\n",
    "    axes[i].add_feature(cartopy.feature.OCEAN, alpha=0.5)\n",
    "    axes[i].coastlines()\n",
    "    axes[i].add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=.5)\n",
    "    axes[i].set_extent([-85, -30, -56, 13])\n",
    "    axes[i].set_title(titles[i])\n",
    "    \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34677.763158</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>0.047845</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>0.078915</td>\n",
       "      <td>0.084504</td>\n",
       "      <td>1.033394</td>\n",
       "      <td>1.023971</td>\n",
       "      <td>1.253966</td>\n",
       "      <td>1.062875</td>\n",
       "      <td>1.071058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34894.078947</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>123.421053</td>\n",
       "      <td>0.047886</td>\n",
       "      <td>0.058219</td>\n",
       "      <td>0.081334</td>\n",
       "      <td>0.092140</td>\n",
       "      <td>1.018854</td>\n",
       "      <td>1.492322</td>\n",
       "      <td>1.112283</td>\n",
       "      <td>1.004868</td>\n",
       "      <td>1.113672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Arica</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34621.353659</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>0.128627</td>\n",
       "      <td>0.150368</td>\n",
       "      <td>0.219307</td>\n",
       "      <td>0.252282</td>\n",
       "      <td>0.964301</td>\n",
       "      <td>1.186351</td>\n",
       "      <td>1.167869</td>\n",
       "      <td>0.918335</td>\n",
       "      <td>1.147524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34617.555556</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>0.013896</td>\n",
       "      <td>0.015536</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>0.027831</td>\n",
       "      <td>1.080644</td>\n",
       "      <td>1.700547</td>\n",
       "      <td>1.484956</td>\n",
       "      <td>0.835009</td>\n",
       "      <td>0.931400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>34619.987654</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>0.057373</td>\n",
       "      <td>0.072412</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>0.126352</td>\n",
       "      <td>1.085850</td>\n",
       "      <td>1.269262</td>\n",
       "      <td>1.236505</td>\n",
       "      <td>1.037081</td>\n",
       "      <td>0.766168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>57485.000000</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.00</td>\n",
       "      <td>828.000000</td>\n",
       "      <td>0.091571</td>\n",
       "      <td>0.132898</td>\n",
       "      <td>0.221663</td>\n",
       "      <td>0.268277</td>\n",
       "      <td>1.638610</td>\n",
       "      <td>1.590918</td>\n",
       "      <td>1.726926</td>\n",
       "      <td>1.636291</td>\n",
       "      <td>1.382136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>57949.326531</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>0.119734</td>\n",
       "      <td>0.157070</td>\n",
       "      <td>0.233521</td>\n",
       "      <td>0.264195</td>\n",
       "      <td>1.215973</td>\n",
       "      <td>1.107323</td>\n",
       "      <td>1.275387</td>\n",
       "      <td>1.244203</td>\n",
       "      <td>1.106254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>Temuco-UFRO_CEFOP</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>58495.000000</td>\n",
       "      <td>-38.745803</td>\n",
       "      <td>-72.615525</td>\n",
       "      <td>119.00</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>0.048010</td>\n",
       "      <td>0.061124</td>\n",
       "      <td>0.089814</td>\n",
       "      <td>0.103530</td>\n",
       "      <td>1.199656</td>\n",
       "      <td>1.237263</td>\n",
       "      <td>1.272808</td>\n",
       "      <td>1.204702</td>\n",
       "      <td>1.155669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>UNC-Palmira</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>58013.269231</td>\n",
       "      <td>3.512295</td>\n",
       "      <td>-76.307610</td>\n",
       "      <td>1065.20</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>0.137504</td>\n",
       "      <td>0.203141</td>\n",
       "      <td>0.316922</td>\n",
       "      <td>0.371170</td>\n",
       "      <td>1.464293</td>\n",
       "      <td>1.334511</td>\n",
       "      <td>1.422013</td>\n",
       "      <td>1.506383</td>\n",
       "      <td>1.138570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>UPC-GEAB-Valledupar</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>57746.684211</td>\n",
       "      <td>9.559600</td>\n",
       "      <td>-73.329300</td>\n",
       "      <td>157.00</td>\n",
       "      <td>838.000000</td>\n",
       "      <td>0.233646</td>\n",
       "      <td>0.330802</td>\n",
       "      <td>0.516816</td>\n",
       "      <td>0.611566</td>\n",
       "      <td>1.438890</td>\n",
       "      <td>1.400320</td>\n",
       "      <td>1.468305</td>\n",
       "      <td>1.452623</td>\n",
       "      <td>1.472595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime                 Name Andes?   season    Unnamed: 0   Latitude  \\\n",
       "0   2014-01-15       ARM_Manacapuru      N  dry_wet  34677.763158  -3.212972   \n",
       "1   2014-01-15        Alta_Floresta      N  dry_wet  34894.078947  -9.871339   \n",
       "2   2014-01-15                Arica      N  dry_wet  34621.353659 -18.471667   \n",
       "3   2014-01-15               CASLEO      Y  dry_wet  34617.555556 -31.798815   \n",
       "4   2014-01-15            CEILAP-BA      N  dry_wet  34619.987654 -34.555425   \n",
       "..         ...                  ...    ...      ...           ...        ...   \n",
       "139 2019-01-15              SP-EACH      N  dry_wet  57485.000000 -23.481630   \n",
       "140 2019-01-15            Sao_Paulo      N  dry_wet  57949.326531 -23.561500   \n",
       "141 2019-01-15    Temuco-UFRO_CEFOP      N  dry_wet  58495.000000 -38.745803   \n",
       "142 2019-01-15          UNC-Palmira      Y  dry_wet  58013.269231   3.512295   \n",
       "143 2019-01-15  UPC-GEAB-Valledupar      N  dry_wet  57746.684211   9.559600   \n",
       "\n",
       "     Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  AOD_500nm  \\\n",
       "0   -60.598056         49.99     620.000000   0.047845   0.053477   0.078915   \n",
       "1   -56.104453        277.00     123.421053   0.047886   0.058219   0.081334   \n",
       "2   -70.313333         25.00     528.000000   0.128627   0.150368   0.219307   \n",
       "3   -69.295685       2485.00     781.000000   0.013896   0.015536   0.021907   \n",
       "4   -58.506411         26.00     800.000000   0.057373   0.072412   0.107979   \n",
       "..         ...           ...            ...        ...        ...        ...   \n",
       "139 -46.499670        754.00     828.000000   0.091571   0.132898   0.221663   \n",
       "140 -46.734983        786.00     196.000000   0.119734   0.157070   0.233521   \n",
       "141 -72.615525        119.00     303.000000   0.048010   0.061124   0.089814   \n",
       "142 -76.307610       1065.20     235.000000   0.137504   0.203141   0.316922   \n",
       "143 -73.329300        157.00     838.000000   0.233646   0.330802   0.516816   \n",
       "\n",
       "     AOD_440nm  440-870_Angstrom_Exponent  380-500_Angstrom_Exponent  \\\n",
       "0     0.084504                   1.033394                   1.023971   \n",
       "1     0.092140                   1.018854                   1.492322   \n",
       "2     0.252282                   0.964301                   1.186351   \n",
       "3     0.027831                   1.080644                   1.700547   \n",
       "4     0.126352                   1.085850                   1.269262   \n",
       "..         ...                        ...                        ...   \n",
       "139   0.268277                   1.638610                   1.590918   \n",
       "140   0.264195                   1.215973                   1.107323   \n",
       "141   0.103530                   1.199656                   1.237263   \n",
       "142   0.371170                   1.464293                   1.334511   \n",
       "143   0.611566                   1.438890                   1.400320   \n",
       "\n",
       "     440-675_Angstrom_Exponent  500-870_Angstrom_Exponent  \\\n",
       "0                     1.253966                   1.062875   \n",
       "1                     1.112283                   1.004868   \n",
       "2                     1.167869                   0.918335   \n",
       "3                     1.484956                   0.835009   \n",
       "4                     1.236505                   1.037081   \n",
       "..                         ...                        ...   \n",
       "139                   1.726926                   1.636291   \n",
       "140                   1.275387                   1.244203   \n",
       "141                   1.272808                   1.204702   \n",
       "142                   1.422013                   1.506383   \n",
       "143                   1.468305                   1.452623   \n",
       "\n",
       "     340-440_Angstrom_Exponent  \n",
       "0                     1.071058  \n",
       "1                     1.113672  \n",
       "2                     1.147524  \n",
       "3                     0.931400  \n",
       "4                     0.766168  \n",
       "..                         ...  \n",
       "139                   1.382136  \n",
       "140                   1.106254  \n",
       "141                   1.155669  \n",
       "142                   1.138570  \n",
       "143                   1.472595  \n",
       "\n",
       "[144 rows x 18 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### concatanating the seasonal aeronet dataframes\n",
    "list_dfs_wet=[]\n",
    "list_dfs_dry=[]\n",
    "\n",
    "list_dfs_wet =[ARNET_2014_wet,  ARNET_2015_wet, ARNET_2016_wet, \n",
    "          ARNET_2017_wet, ARNET_2018_wet, ARNET_2019_wet ]\n",
    "list_dfs_dry =[ARNET_2014_dry,  ARNET_2015_dry, ARNET_2016_dry,\n",
    "           ARNET_2017_dry,ARNET_2018_dry, ARNET_2019_dry]\n",
    "\n",
    "ARNET_conc_wet =pd.concat(list_dfs_wet, ignore_index=True)\n",
    "#seas_ARNET_wet= ARNET_conc_wet.groupby([pd.Grouper(freq = \"Y\"), \"Name\", \"Andes?\", 'season']).agg(np.mean)\n",
    "seas_ARNET_wet= ARNET_conc_wet.groupby('Name').mean().reset_index()\n",
    "ARNET_conc_dry =pd.concat(list_dfs_dry, ignore_index=True)\n",
    "seas_ARNET_dry= ARNET_conc_dry.groupby('Name').mean().reset_index()\n",
    "ARNET_conc_wet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seas_ARNET_dry['Latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KlNaN112joQP6qOdVneTnlz5LdNaEw7Hx8TntIldZk71K6YHcAAAAAAAAuLSV+OBGSRr/yEhDcOOZ6Fi9/8UPevL+2yzsFWCN3fsidfqsMQNmhzbNi7wfySmpmvntL9q655DLuqAyvrqlW301r1M42YR//Xurjp44K0ny9fFS/WpB6tqynuK8ymvPMeOMiSfOxOiNz37WQ7ddp+oh5U23nXUgz9c3/TZdPstgjd1uV2xcvIKDjEGPcDVx7H3qf9djSkxMKrI2g8oGqlqVEGegYpXKlVStcoihrFqVEJUvF3QhQNWR/SB1hv59uius0WUaOOJRRezYbVj30psztO7fzZrz/quqWKFcIR1V/sWfMw6EBwYUbCCyWcdPnNL4l6Zp/sLlhsG2nAQHldWAvt11S//euqpjG/n4XPxrlL+/n3x9fZSSaVbWrEGKeZV11tGyxex8AgAAAAAAACi+6tSqoWE399Vn835ylr37yTw9fv+dKhfMWANKprXrNxmWH7vvDk2dNM6Svhw9cVqzv/9VB4+eyHb9ZXVrq1un9mrZvEmexidyY7PZFFKpgm7s00MVypXV2dMn5LCn6cz5MeIyZcqodu3aSrM79OMva/Tftr26o/+1qhpScGOzOc/caBwTPXOGmRvzolaNwplhsyh4e3urSkhFVasSIodD2rApwrB+4bLf1K77QM3/9G21aNrIol7mLCEhUQ6Hw1DW46rOCqlY4UIgYtlAl4DEwIAA4/L5/882ma091bUsr7L0zeFwaP3GCM3++kd9/eMSHT56/KK78PPzVe9rumrogD7q1+NKBWQzRpk14au746HZjdlml0wWAAAAAAAAlxaCGyU1a9JAN/W+Rj8s/tVZ9uZH8/TQnQMUUKa0hT0Dit7af42zNlatXLHIB0OSU1L13ldLtevgMZd17RuG6ObOdVXGv3BuX3a7Xeu37jOUVQwO1O19O8jPz1d/RBzTj2sOKDn1QiBadFyC3pq9SKOH9VaNKu4P6p05G6Olv682lPmfz3SYdSBPSp+9keDGi+t1VScd3/SLtu/er9TUVNntDqXZ09L/TUvLtGxXWppddtmUZrdfWLbbXbdPS5Pdkf6vzWZT5UoVzgcsVlHVypVUppA+OxrWr6PVi7/UqDHPad6PSwzrlv0errY9h+jbj95Qm8vDCqX9/Mo6+FScghsdDoe63Thcu/YeyHW70qVL6YaeV2lo/9667urO8vf3y9hBntsqGxig05kG3wsqU2kQA3kAAAAAAAAA8uGpR0Zq9tcLnEEYMbFxeueTuXr20VEW9wwoerFx8dqy3ZhMsn+fay3py879hzXjq8VKzJIEVZKqVg7RsME3ql6dWgXe7oGDkfpt5Z+qUa2KalSrotNnzmrrjj2qVbu2IcDqwJEoTZk1X/ff2leX1a5eIG3nOHNjloSvZ6KZuTEvrEhWfDGBAWUMSV+rnP//qlUqO8uqVg5RpYrlnclgHQ6HXnt7pp558U3Z7RfG4/fsO6iO1w3RjNef17BBN1h1SNnKmuxVkj5/f6oqh1S8UGCzFWGPsrdt5x599d0izf3+Z+3ed/Ci23t5eemaLldo6IA+6t/nWpdZVbMqG2gcB85LYtnsxMQa6/n7+1kyky4AAAAAAACKFsGN5z09epQhuDHq5BkNun+Cvv/wfxcCGYASYM0GY3Bjh9bNZSvCh+2paWma+e0vLoGNfj5eGn5tQ7WoWzizNWY4ExOv5BRj5sObe7ZXqfP3gSubVVPT0PKatWy7Dp+6kG0wPiFJb3+5VI/d2VeVK7gGIl6Mw+HQXY9N1v7II4byLu1bSUoPjvL29lZaWtqFvp6NVZ3QfDdVIpUNDFC7lnkM+PM28dFoyyaTZgELDCijL2dMUYe2l+vxya8rNfXC9Xog8oi6XH+H3n3lWd11a/9C70tOomNi9cq0DzXtw88M5YUV9OmOo8dP5BjY6OPjo+uu7qSh/Xvrhl5Xmw7KLBuQJbixgAbzgsoS3AgAAAAAAAAg7xpdVleDru+pr39a6iybNvNLPTrqtmKVnA4oCv/8F2EInvLx8VHr5k2KvB+bd+7XrK+XKTXTGKCUPrtij6u7qG+Pq+TrW/CBPXa7XT8sWGQ4BxXKl9Nbr03Wzt0H9MuqcMNsdIlJyXrn8580akhvhTWobapth8OhcwmJhrILMzcaA6jOnCW4MS/q1a6p9155WlPfn60Tp86cT95qTOaadXZBd9hsNoVULK+qlUPOByhWuhC0mBHIWDn9/wNzStLplfN4qs1m05OP3KO2LZvrllFjdPLUGee6hIRE3fHAE1q7fqNef/5J+fkVj9/xxGWT1LQ4fabuO3BIdz70lP5cuyFP23doc7mGDuijwTdcp6pVQiTl7bpxnbnR3fHQOMMyyV4BAAAAAABKBoIbz2vbMky9ru6kpb+FO8uWrFyrWx6epHnvPi9fX04VSoa1WYIb27cquhngHA6HPv9plbbuOWQoL+Pvowf6NFGdKoU/S2HUaeMAWUBpf1WtVM5QFhJcSqNvaKb3F23TvuOxzvLY+AS9PWeJHh9xvYIC8xfEjOKb8QABAABJREFU9caMOfpx6UpDWae2LdSvR1dJ6QM55YLK6tSZs871ZCotuWw2m0bfc7tat2iqIaPG6ljUSee6pKRkjRzznNas36jpL41XqVL+RdavlJQUzfz8G0167W3DYGOGmtWLdhbY3FSrEqIWTRtq09adhvKRwwbqlWceVYXyuWcfzau3PvxCBw4Zg5azDtjnVXGeCRMAAAAAAACAZxj/6ChDcOOpM2f1+gefa+LYey3sFVD01v672bDcMqyRSpcuVaR92LLrgD6ct9QQYCilz9Z4x9D+qlOrZqG1ffTYcZ05G20ou6prZzVr0ljNmjRWy+ZN9Pm8H3T8xIUxqJTUNM34apHuu7Wvml7m/kySs76Yr30HjePBGYFR5ctlnbnR2Efk7L47btZ9d9yc43qHw3Eh2NHmo7S0NJcAyAvLabLbHen/OtL/DQwoo8qVKqQH29q8CvVYru3WUet//V6DRjyivzdsMqx796M52rApQl9/NE01qlUp1H7kxc49+w3LNputyO8luXn02ZcvGtjYvElDDR3QR0Nv6qO6td277xRacGPZQLf2AwAAAAAAAM9SuE8cPcybzz+pcsHG4Kkflv2hOx970TBbGnCpOpeQqI1ZAn06tG5eZO2v2bhL/0TsNZSV8ffR6BvCiiSwUXINbgzJYRbGMv4+eqhfU9WvZlx/OjpOXy36K1+ZL/9a95+e+t/bhrJKFcpp7vsvGzKxli9nPAdkKkXXDm20fsU36nJFa5d1s76Yr2433qmDh44Wej8cDod+WvKrWnS7QQ89+XyOgY13Drmp0PuSVzabTbPemCwfH2PygsW//FEgmWsdDodeeGOGHps4xWVdo/p13Npn1kFAZm4EAAAAAAAAkF+XhzVSvx5XGsomv/6BPv7qB2s6BFhk7XpjwFT7IhwTlaRdB45o1teugY0tmzfV+DH3FWpgoyRFRZ0wLJcvF6wru3ZyLterU0vjH7tfLcIaG7ZLs9s1c95i7T3o3vjTv5u36ZFn/pel7SB1uaJN+v8HZwluZDy0wNhsNnl7e8vPz1elSvkrIKCMgsoGqlxwkCpWKKeQShVUtXIlVa9aWaE1qql2aHXVqxOqy+rWUqPL6qpGtSqFMotoTkJrVNPKBXN07/ChLutWr/tPba4doJV//V1k/cnqyNHjunv00+o7dJShPKBMGdlsNot65SpzktzM6tUO1dNj7tXmVT9q48ofNH70PW4HNkpyuZe5G9wYG3/OsMx4KAAAAAAAQMlAcGMmjRvU1eKv3ldggHHGtbkLftGoJ191eRgHXGo2bN6u1NQLgbxeXl5qe3mTImn7TEy85i9fayjz8/HSA32aqEbFontgnTW4sXIOwY2S5O/rrfuua6zQSsb+bdp5UOu27M2hltGJU2c09L6nDQHUNptNX7zzompWN2aadBnMY+ZGKH0Gwl/mf6RHRg1zWffPfxFq02OwFq1YpdTU1EJpf/3GCF3b/07ddPsD2rF7n8t6Hx8fPTRymDb8+r1qh9YolD64q23LML00/mFD2eGjURr52ERTAY4Oh0NPvviWJk55z2Xdc2PvU8tmjbOpdXExsVmCGwMZzAMAAAAAAACQf88+5jpL46hxz+ur7xdb0Bug6DkcDq3JEtzYoU2LImv/xOloffDVYqWkGhMsd2jbSncPG1QkAWRRJ4wBT1WrVHYJyPLz9dWoO4aofZvLDeXJKal678ufdepM/sYqo2NiNXjkY0pKSjaUf/j6ZJUq5S/JdebGmNg4ElGXYP7+fnp/6mR9/PbLzmskQ9SJU+o+cITeeP+TAklcmlfx8ec0+bW31fCKnvpkzrcubTdv2rDI+pIXI4e5zubZ5vIw7Vy7WC+OH62wxg1M7d/hcGjCK9O1cNlKQ3lqqnvv26wzN5YNYDwUAAAAAACgJCC4MYsrWrfQwo9fU+ksD0Y//XaxHp74VpE+FAWK2poNmw3LzRvXV2BAmUJv1+Fw6Muf/1RCloGs4dc2LLIZGzOcyEdwoySV9vfRA32bqGxp4yDjN0tXKzr2XA610tntdt3+8AQdPhZlKH9m9N3qeWUHl+2zBjcePZ59lkWUPL6+vnrrxSc15/1XVaa0MUD/1Omz6nfbgwqs217Nu/XX4JGPaeJr72ru94u1MWKHEhOT3Goz8vBR3fngeLXrMVi/55AVtX/fHtry5wJNf/lZVapY3q12CtvY++9Ujys7Gsp+XPKb3v90nlv7s9vteuCplzT1vU9d1r38zGhNGne/W/uVpBhmbgQAAAAAAABQANq3bq4Xxz9iKHM4HLrjkQn6fvGvFvUKKDoHIo/o+IlThrKiCm60Oxz64qfflJhlXPSKNi01bPCN8vb2LpJ+ZA1urBwSku123t7eumNIf7VrZTw/CYlJ+uKnX/P8+wmHw6GRY57Tnv2RhvJHRg3TwH49nMvlywW71D1LwtcSb/gtA/TXorkuMwumpaVp3HOvaujIMYqNi8uhdsFIS0vTx3O+VcP2PTX5tbd17lyCyzY1qlXRO69OLNR+5NfIYTerX8+rDGXrN0borRmzTe/b4XBozIRX9NIbM1zW9bq6s1v7dEn2yngoAAAAAABAieBjdQckSTav9P8MZbbst80Lu7kAxG7duuqHT9/S9Xc8ouTkFGf5+59/r9IBZTXluTEuWQudEs643a7Ny9xAhcNhYmZJh8lBEpuJtk3VtTA+18w1KkneJjJumnp/5Jwh7+8NWwzLV7Ru5rq9messh9drzcZd2rrnkKGsfcMQtahbwf223JCWZteps8ZBj4sFN0pS2dJ+Gty1nj5atsNZdi4xWV8tXq17B/eQTdmfs5emfaRlK9cYyq7p3FYTHxsp6fx9zH5htr2QiuUM27727ifqc01HtWmRy+yaXiZu8zYTdc1cJ5JkKpDcZBC6mfuKqfuCyfuZI023DOij5k0aaOBdY7Rr7wHD6uTkFEXs2K2IHbslLb/QqpeX6taqoaaNLlPjBvXUpGE9NW1YX40b1FNQ2UCXZmJi4/Tq27P05gef5RgY2bZlc0194Sl169Q+D/0283qZvA/LIS8vL306/UW1vOZmnTh14TN83OTX1a1DGzVrkvdspampqbrr0ef0xfyfXda98/LTemD4kExN5/89EpsluLFsQJn8n79cPgPyuAP3q1qZIMJ02xbN3u3R58wM62ZLN5XIxOxnnxmWvl4l8Zx5ar/N1vfQ4y6pnwGm3l8e+vlj6T3FQ9s2+fzMFNN/M5qob+bv5IKoDwAAUEw8/egoxcXF6ZW3P3aWpaWlaeh9T+qHT95U72u6WNg7oHCt2WCctbFihXKqXye0SNpe9fcW7T5w1FDWIqyxhg2+UV5eRTf27RrcWCnHbb28vHTH0P5KSEzUlm07neU79x3Wn/9EqGu7Zhdtb/rMLzR/4XJD2RWtW+i158YayrIme5WkM2djVLFC8UyiiaLTqkVTrVv+rW6//wkt/mWVYd03Py3Rlu279PSY+9S0YX01uqyuypQpncOe8m/573/p8UmvaVPEjmzXBwSU0RMPj9Jj949QQBEkj84Pm82mj956US2v7q+jx084y8e/+Iau6txerVs0dWu/aWlpunfcZH08Z77LuqmTH1fPqzu79fwm68yN2Y1XAwAAAAAA4NLDr3Fy0OPKjvpm5lQNvHusUlMvBBe98cFsBZQppcmPP2Bh74DCsSZrcGPLsEJvMzrunOavWGcoCyrjq5s71y30trM6dTZW9iw/pg0pn7eZI1vVq6jW9Stqw54LWV437zyg9RF71DbM9Vh++eNvTXr9Q0NZtSqVNOfdF3PMyDqw77Wa891i53L8uQT1u+NRhf/0serWqpGnfuLS16xJA/299CsNf/gZ/bjkt4tub7fbtWd/pPbsj9SCpcbta1Srkh7o2LCemjSop6TkFL381oeKOnkq233Vqlld/5swVkMH9CvSAXizqlUJ0SfTXlS/YQ86yxITk3TLfU/o7yVfqXTpUhfdR1JSsm657wn9kOWce3l56eM3n9cdg6/Pc3+Sk1N08PAx7T14WHsPHNLeg4e1/+ARbdu1z7BdUCCZSgEAAAAAAACPZzrxg/vPYl969jGdS0rW9A+/cJalpKRq4MhxWjjnPV3TtUPOldNScl6XF6aStlqY0MWqJFNmk66aSRZoM5kk12SC38KwdsNmw3KH1i1ckxubea1zeL1OnI7Wj78YE59WLF9Ow28dWGQzNkpSQkKCyyx3uQU3SukzOI647Wa9OPVdnTkb7Sz/fnm4mjasp4rlgyRH9gkW1/yzUY9Pft1QVqF8sObNel1+/n7nS9LPWenSpeXn52tIQr1h0zZdVq9Ozp0zmRA5x8TWeeBwWPiTGzPXqNnEPabuSe7XrVC+nBZ8+YGen/qunp/yrmHdtp17dPv9j5/vnk11atVQkwb11aRRfTVpUF9NG9VXk0aXKTgob+P/krRl2049Mek1Lfnlj2zXe3l56a5hgzT5ydGqVrWy28d1UaaucbtCQipq9nuvqufNdzuTPKakpOqWe8Zq/S/zFZjbmGM2l1lKSoruePApzfthsaHcZrPpg6mTNOr2QRkl+e5tTNZkr26Ph5r53HO/qvnkvChSpr9foUSw8jKxNMFtCWTlJBtWsvI6s+z9VULfW1YmazWlhL5eZpiZ/IDvRwAACxWL4MaEhESru5Ct63teqTnv/U+33PeU7PYLX+xeeONDlSldWk8+NMLC3gEF69DRKB06GmUo69C68IMbf1+3TQlJxgHwW7rVVxn/or89RZ2OMSwHB5ZWKeeA2sUN7lJPOw9HKy7xQkD0olX/qk3TOoZBsaPHT+q2hyYYZofy8vLSV++9pCohFXPc/429rtSwAb31RaYAx+MnTqnPsEf05w8fqWKFcnnuKy5twUFlNf+Tt/T2rC/15ozZOnjo6MUrZePw0eM6fPS4lq8Mz3W7oLKBGj/mPj1yz515CgQsjvp076pHRt2m6TPnOMsiduzRuMmv691Xnrlo/eemvOsS2Ojr66Mv33tVA/t1N5Q7HA5FnTilfQePOAMY90Ue0b4Dh7X34GEdOhpl+N6Rk6DA4pX5FQAAAAAAAIBnsdlsevOFp5SQkKSZn3/jLE9MTNKNtz+kJfM+VOcrWlvYQ6BwrF1vnLnxijYtCr1Nu8OhL376TckpqYbyYUNuUil//0JvP7MTWZJYent7q0IeZkYsXaqUbht0o96ZOdtZlpScoi9+WK6H7xwgr2x+A3nq9FkNGfWYIaG0JM1+9xXVqlndZXubzaY6oTW0c89+Z9nIR59Rg3q11crNGeZwafHy8tKkJx5Wu5bNdfsDT+hsdIzLNg6HQ/sOHNK+A4e0aMVKw7pqVULUtNFlatywvpo2rK8m5/+rHFLROaZ/7PgJPffKdH0859scx+yuu7abXp30pJo3bVTwB1kIru3WUY8/dLdee3uWs2zX3gN65On/6ePpL+V5P4mJSRo8cowWLvvdUO7t7a3Z776iWwb0dbuPDodD0dGxhjL3gxsBAAAAAADgSYpFcGOvQXfps3dfVYe2La3uiotB1/dUYmKyho82BiKNf2maypQupYfvvsXC3gEFZ+2/xlkbg4MC1ah+7UJtMyU1TeH/7TKUtWtQSc3rVCjUdnOSNbgxpEJQvuoHlvbV4K719PHync6y46fOateBY2pYp5okKTU1Vbc88LSiTp421H3xyfvVrUPuPxCw2Wya9fpzOnL8pH7968Jslzv2HNBNd43V8rnvqVSpoh38RPHl5eWl0fcM0+h7hunU6bPatmuvtu3cq60792j7rn3atmuv20GPGby9vXXPHYM16anRCqmUc2Cup3j12TFaGf6PNkbscJa9/+k89byqk2687upc6367YLlLmc1m08o1/+jwsePae+Cw9keen4nxwCGdK4DEDnVDXQf9AQAAAAAAACA/bDab3p/ynBITk/T5Nz85y+PPJajvrfdrxfyP1LZlMwt7CBSspKRkbdi8zVDWoQiCG9f8t127DxjHZbp1aq9Gl9Ur9Lazijpx0rBcqWKFPM8c2bTRZep8RRv9tXa9s2znvkNat2m7rri8oWFbu92uOx8ar8jDxwzlT40epT7du+XYxv0jbtWYZ//nXI6LP6e+t9yr1UvmqnZojTz1E5e+vj2v0j8r5mvg8Ie1MWJ7nusdPX5CR4+f0C+rVhvKK5QvpyYN66l2zer6ccmvio8/l2395k0bacrzT6nn1V1N9d8KL4x/RL/9uVbr/r0we+2nX32nXld31pD+fS5aPyEhUdcPu1+//rHWUO7n56uvZ72pG6675qL7SEpK1oFDR9KTvx5MHzfdd378dN/Bw4qOMQY3BpUNzOPRAQAAAAAAwJMVi+DGfQcPq0vfW/XEwyM18fGH5J+PmdKKwu2D+ikhMVH3PfGioXz0s6/Kx8db99852KKeAQVnzYYIw/IVLcPk5eVVqG3+u/2A4hKSDGW9Wtcs1DZz4xLcWD5/wY2S1KpeRVUpV1rHzyY4y1at3+YMbnxuygytWvOvoU6fazrriQfuyNP+/fx8NX/WFHXtf7e2bN/jLP9r3Ubd/vAEzZvxSqG/bvA8FSuUU5crWqtLlgzbcfHnzgc67tfWnbu1/Xzw4579kRedOfD6Xlfr1efGqnGDepK3b2F2v8j4+/vpqw9eU9ueQwzBh3ePeU5tWnyjmtWr5li3S/tW2nfwsKEsOTlF73z0VaH09YE7B6p+HevulwAAAAAAAADy780Zn+uFJx+Ur2/xeqbq5eWlj6a9oITERH27YJmzPCY2TtcNuUe/ff+pmjdtmMseAM/xX8R2JSenOJdtNpvatQwr1DYdDod+W2OcLbJi+XK6qW+PQm03J1FRJwzLlUMq5av+gOt7aeuO3TpzNtpZ9mv4v2rfooFz5jtJeu2dj7RoxSpD3Ss7tdPzTz6U6/4fHjVMf2/YqK+++9lZdizqhPoMvUd//vylypcLzld/cemqVydUfy36Sh/O/lorVoZr687d2p9lvC6vTp85q7/WbtBfazdku75qlRC98PQYDb9lYJ6DgYsbX19ffTljqlpfM0CxcfHO8nvHTtQVbS5XnVq5Bw+/89Ecl8BGSfryg6nOwEaHw6HjUSfPJ3w9qL0HDmn/wYwEsJE6fDTKkFj+YsoGlsnztgAAAAAAAPBcxSK4UUrP2vfKtA+1aMVKffbOq7q8WSOru2Rwz+0361xCoh6bONVQ/uBT/9OJU2c0Ycw9hgf1gKdZu8E4c+MVrQs/C+8fG4wZFBtWD1LV8tY9nD6RJbixcj5nbpTSB0C7NK2i+eH7nWUbtx/Q2dhz+mvtBr3yzqeG7WvVqKrPpk3KV0BicFCgfp49TZ1uuEuHj0U5y+cv+lVjJ7+pNyePzXe/UTIFBpRR25ZhatvKmJE4KSlZu/Ye0Nadu7Vt515t37VXW3fsUdTJU2rRtJGeGj1KV3e5wqJeF67GDerqrRef1D1jJzvLTp+JVvOrBqpOaHVVrVxRVUMqqcr5f6tWrqiqlSvpsXvvkJ+fr2Z/s0ApKakF0pfy5YJUN7S66tWqobq1a6T/W6uGmtYPVc1qlQukDQAAAAAAAABF540PZuvXP9bq0+kvqmmj+lZ3x8DHx0dfvP+qEpOStHDZSmf56TPR6nHzSK386TM1uqyuhT0ECsaa9cYgwyYN6ik4qGyhtrnn4FEdiTptKBvcv69K+fsXars5yTpzY36DG0uXKqVBN/XRh59eSPB46NgJ7Tt0TPVC0xO+rgxfp2f/N93YTqWK+nLGFPn45P4zFS8vL308/WUdPX5Cv//1t7N828496n/Hg1ry9UcqVcqac4fip0yZ0nr0vjv16H13SpLi489px+592rZrr7bu2K3tu/Zq28492rX3gNLS0tza/7gH79a4h0YpMDCgoLtf5OrXraV3X3tOdzzwpLMsJjZOt903Tit/+jzX9+eufQezLR/12AR9/OV87Y88rH0HDyshUxJZs9oxezQAAAAAAECJUGyCGzNsitih9j0H6d1XJ2jk7YOs7o7Bo/cMU0Jikp55+W1D+aQp7yvq5GlNe+EJeWZ+NpR0KSmpWr/ZGGh4RavCzVAaefy09h4yZgXtGpbzrGiFLSUlVWdi4w1l7gQ3StIVjSrrp78PKiU1feY7u8Oh75eFa9xzLxu28/X10bwPXlbFCuXy3UZojar6+fNp6jZgpGIy9XvarK9Uq0ZVjbnnNrf6DkjpMxg2a9JAzZo0sLorlrj71gFa9lu4vl243FkWHROrjRE7tDEil4pKn101MKCM4s8lXDTrqK+vj+rUrK66taqrbq0aqpcpgLFuaHWVL5fDPSgtJftyAAAAAAAAAMXePxsj1KbnEL00/hGNHnWbvItR7lQ/Pz99PetN3TDsQa1YtdpZHnXylK7pP0JL5n3IDI7weH9v2GxYvqJNixy2LDir/jEOLlSuVFFhja0Zg3E4HNkEN4bkez8tmjZSxQrlder0GWfZqr+3qF5oNR2POqlb7hknu93uXOfl5aUvZ7ymalXy1pa/v5++++wddel7q7bu2H2hjdX/aPhDT+nLD1/PV/JYlBwBAWXU+vIwtb7c+HuH5ORk7d4fqa07dmvbzj3avnOvtu7crR279ykxMcllPzabTcNvHaAXnhqt6tWqSLZL59dAwwbdoGW//akvvlngLFu97j89+fzruu/OIapaJUSBAWVcErzfPuh6zfl2gUvw4pmzMS6ztJrh7e2terVr6sG7hqpTu5YFtl8AAAAAAAAUX8UuuFGSUlJS9PD4FzXw+p4qXy7Y6u4YjH/kbqWkpmrSlPcN5e99Mk8nTp7W7NfGyd/fz6LeAe7ZvH23ErI8sC/s4Ma1m3YbloPK+KpFnQqF2mZuTpyJVeY4JJtNCinvXpbWMv4+andZJYVvT59VMS0tTRNema4zZ40zQ06ZMNrUDJktmjbQ/JlT1HvYw0pNvZBlctzzbym0ehXd3K+72/sGSjKbzaYZUyfq73+36ODho/mqm5ycouTk3IMPvb29Nej67nr7hcfdCm4GAAAAAAAA4NmSkpI1btJU/bjkV33y5iTVq13T6i45lSrlr+8/m67eQ+/Vn2s3OMuPHj+hK2+8Uz99/o66dGhjYQ8Bc7LO3NihTfNCbS867pz+27rXUNa1UzvLAvPi4+N1LiHBUJbfmRul9GDFrh3b6oefLySK/Ddil27q0VG33f+EjkUZAygn/Z+9uw5vInnjAP5N6u5Kjba0QHF3d3d3dznsB3fHccBhhxx6uLtrcYfixQpUKNTdXZLs7w+OlG1qySZNQ9/P8+Sh++7szMTDzr4z86eiTfNGUrVhbGQIz+M70bjTQERG502ae/LCVdjb2eDvJfOl7jcpvzQ1NVHV3RVV3V1ZcaFQiKCQcHzyD8RHv8/wDwyCtZU5+vfojJrVKiupt4q3ZfVieL14gy9BoeLYhn/3Y8O/+wF8W7HS2tIc1hbmsLGygJWlOawtzTFv6ljcuPcIT1++5dS+makxnB3s4OxoByeHCnB2tIezox0qOlSAfQVraKj/PMmkhBBCCCGEEEIIIaR4ZSK50dTEGNaWZnj3wU8cy87OwePn3ujWobUSe1awxb9MhJmJEWb8upq1KtOpSzcRHxeHs9uXwdBAT4k9JEQ6T719WNuVKtrDzESxicWBYTGs7SaVraCmprzZNWMS2ImHJgZ60NCQ/SOyaVVrcXJjUFAQIiLYCVJ9u7TB9DEDZa7/u7bNG2DPusUYOfMPcYxhGExZuApd2jaDrp4+5zYIKY9MjA1xZu8GdB06BTFxCXKtWygU4vj567j/5BW2rfwfenZsJdf6CSGEEEIIIYQQQgghquHhU2/U7TgETy4dQOVKFZXdHTE9PV1cPvov2vcbixev88aQkpJT0GHAeBzfuRY92jdXYg8JkU10bDy+hoSzYo0UvHLjE+9PEP6wgqGGhgYa16ut0DaLkn/VRg0NdRjLOOF0kwZ1cPn6XQgEAgCAQCjCtP+twJ2Hz1jlOrZuikWzJsjUhoOdLa4c24kW3YciLT1DHF+3dS/sba0xY8IImeol5Ds1NTW4VHSAS0UHdOtY9q5PUhRDA30c3bEWzboOFb+Hf5SRkYkvQaGs5EdpqKupwdHeFm4uTv8lL9qJbxUd7GBoUMx1DIyo6P2EEEIIIYQQQggh5KeivEyiHyQlp+Du+YOoV4u9gtmTF9xm+lKkqaMH4dj2VRLJT3eevEabobMRHSvfRAhCFOnZ6w+s7UYcVhMsCYFQiIiYRFaskq2hQtssTv7kRgtTbv2xt9CDtsa32QRzcyVXcVsydwJ4PB6nNr4b3q8rFs0Yw4rFJSQhNS1dLvUTUl7VrVkVoa9v4vXtU7h67F/s27gMK3+dhZkThmFQr05o1aQeKrtWhLGRbKu8RkbHofeYuRg0aSGiY+Pl3HtCCCGEEEIIIYQQQogqSE5Jw7YDp5TdDQmGBvq4dmInmjZgJ2FlZWWjz6iZ2HvsvHI6RggHz7zZqzbq6+miqpuLQtv0/hjI2q5Xuzp0dXUU2mZR8ic3WlpYyDxmqa+nhzo1PcTbEREROHbmAqtMBRsrHNq2mtNKlbWqV8HpfZugrs6+NmP2bytx9dYDmeslpLxrUKcGli2coZC6BUIhYuIS0L1jK2xesQhzJo9C7y7tUNOjcvGJjYQQQgghhBBCCCGk3CkTKzeKRCIEBoWgbYvGePkmb/bPJy9eK7FXxRvQoyPMTIzRe/Rs1iyB3j4BaDZgOq4f+BvODrZK7CEhJZN/5caGtT0KKSkfkbFJEAjZM+3ZWyj3BHb+5EZLjsmNfB4PduZ6+ByZAgcHB6ipqUEoFIr37zpyHv8sncOpjR9paWqyth3tbGBpbiq3+gkprzQ0NFDTwx01PdzZO35YuRn4dkFPdGw8omLjEBUTj6iYOETFfvs3+r+/I6PjEB4VIzH76clLN3Hr4XNs+PMXDOvbRW6Jz4QQQgghhBBCCCGEkLJnxvihuHT9Hmv1uMfP3yivQ0UwMTbC9ZO7MHjiXFy6fk8cF4lEGPfLEkTHxuN/08fQOU2iMp6+Yic31q/lATU1NYW1l5MrQGQMe1LkhnVrKqy9kpBMbjTnVF+jurXw/NW3Sas/ffrEGj5RV1fHiV3rYG5mwqkNAOjQuhl2rl+KMTMWiWMMw+Cv9f+ic4c2nOsnpLyaN20shEIRDp+6iPDIaLlOoJyalo4p85fh+Lmr2LluCdxcnORWNyGEEEIIIYQQQgj5uZSJ5EY+nw9rSws0yTfz5/PX7yEQCCRm4CtL2jZviLtndqPL0KmIjc9biS4wOALN+k+H577VqFXVVYk9JKRoDMPgc1AYK1a3RmWFthkSxV6hzNxQC7paynufMwyDmPhkVoxrciMAOFh8S27U1dVFlSpV4OOTl0S668g5/DpzDCzkMJgHABd+uKgAAHp0bEkXExBSirS1teBobwtH+6InNQgJi8TkBctx9c4jVjwhKRkjZ/6BY+evY/vqRXCoYK3I7hJCCCGEEEIIIYQQQpREU0MDe/9ZhtZ9xohj7z4FIC09A/p6ukrsWcF0dXVwZt9GTJizBPuPnWPt+3XlZkTHxmP9n3M5rcpGSGl57v2etd2wbg2FthceHQfRD9l+PB4PDnbKnRw5JjaWtW1hbsapPkf7CuK/dXXZn2E62lqo6i6/lTFHDe6Dy9fv4uyVm+IYjYcSwg2fz8ei2ROxaPZEAEB6egaiY+MRGR37bTLX6Nj/Jnf9b0LXH275J3QtzIMnL1GrTV8smTcVv0waUaavASOEEEIIIYQQQgghylEmzhjNmDAcdrZW0NLUYMUzMjLxzucT6tSoKl2FPI6DZxpaUhWvW68eHnmeQKcBY/A1OC9JLCo2Aa0Gz8L5vevQqknd4ivKTim+TFFEwuLLFIbHcUZKLofzObwMRSU7WVooYTa347ng+jqVFcNeMTEnOwdMvhXITI0MJMoVdrx0vg0uhUSykxsdlLxqo+/XCKRmZLFiVmZGnOv9cTXKWrVq4ePHjxCJvj1+mVnZ2LjrKJbPn1R0Jfzi31yh4VHwfu/LivXq2BIAU6LjC8XpueaIy0Ak188zKGkQlPPgK4f7zbXtfJ8h0h2rxNeZEjjY2eDy4S04cvoiZi1eh4QkdmL1tbteqNZ6AFYumobJI/oVckEQh8ebKy7PtVLbVuLrjNP7Q4mPN8fHLP9vi1KjzM8UpT5fynxvcnjMVfU1zrXf5fV+c6HM7wCV/TxT0e8fTp8pyvwO4Ni2SFVfZyraNld8TWX3gBBCCCEqLiQsUrxanFD4bWxPKBTixZsPaN20vpJ7VzB1dXXs+WcZrCzMsHrTbta+TbuPIi4+EXv/WQrNfGO9hJQlDMPg+WsfVqyRgpMbQyLYqyRaW5pDW0u6axHkiWEYua/cqKOjDUtzM8TExaNKlSp48+aNeF9qWjq27j2GX/9LmuKKYRh4v//IirVr1UQudRNCvtHT04Wzni6cney/BQo5VygSiZCYlIKomFhWwmNUTBz8Pn/F5Rv3WOWzsrLxv2XrcfLCNez5Zylqeih2wm1CCCGEEEIIIYQQolrKxBSasyeNBABYmJuikrMja985z9vigb2yrJKLEx5dOY6aVSux4imp6eg0dDrOet5RUs9Uw73nPhg6bwOGztuABWsPYtOhyzh9/QmevPFDSEQscnM5JjGSQmXn5ErEtLUUe6FeZGwSa9veXHnJjQzD4NFrf1bM0swQ5iYGnOv+MWnTwMAAlSqxPx+2HjiN5JQ0zu1cuPGAtW1ibIjmDWtxrpcQohg8Hg/D+nbBx/unMLBHe4n9aekZmP7rGrTqOwF+n4NKv4OEEEIIIYQQQgghhBCFadW0PnR1dVDLw50Vf/LqnZJ6VDI8Hg8rf5uN9csWSOw7eu4qeoyYgbT0DCX0jJCSSc/IlHiNVqtcqZDS8hESyV4l0V7JqzZGR8cgJ9/YsKWFBed6v69GaWxsDBcX9kqNG3ceQrqcPhvef/RHUEg4K9arczu51E0IkQ6fz4eZqTE8KldC2xaNMbRfd8yZMhp/L5mHi4e34cap3XD6YWXX77zffUT9DoPw28pNyMpS4mTkhBBCCCGEEEIIIaRMKRMrN/64KlHjerUQ8CVYvP3Xhh3Yd+wchvXrjpEDe6CKm0tBVZQJNtaWuHdmJ3qNmYP7T7zF8ZycXPSf8D/8u+p/mDCsjxJ7WDZtOeKJWSv3FrnCD4/Hg5WZESpYmcHW0hQVLE1RwcoUFSxN4Gxniaa13QtZ3YoUJytb8oRx/lVU5S0zO4e1baKvvFUPvobHIiImkRVrVtsdPM6r6AEmeuz7Vbt2bfj7+4tf68kpadh28AwWThvJqZ0L1+6xtru1bQZ19TLx8U4IKYKluSmO/bsSg3p1xNRFqxERxb7I4dHzN6jVYQj++GUC5kwcBg0Nel8TQgghhBBCCCGEEKLKOrZqggnD+gIMg8b1a+LVu7wVyJ6+fAuIZJjsk6cme4fUpF9BbtaU8bC0tMKoqfMhEOT198b9J2jbfwIuH/kXFuamxVck5DKxKZcxHI4rp3MfPgLDMGAYRrqxTR7XcVA5dFzFZecbnwQAXR3twg+Qw1hhaL7kRgclJzc+fvqcta2vpwcDA+6T0Nrb2eDlm/cAgDp16iAwMFC8Ly4+EbsPn8bMCcOLroRX/Hvz/NWbrG1He1vU8HADj8/hcxAodGW6EuHyGcz58gYOr1HOnykccHpvcey3HN7XysGl3xzvs4yHt2vVFO8fnMdvqzZh087DrOuBBAIBVvyzE2ev3MLuDUvRpEFt+TUsD1x+Kqjsa4yUOnqtkJJQ1dcJl99WSqWijzdX5fRuc6Kqr3Eu/3fhei6FC1V9vLk8ZoxIft2QFpf/Xxd4rk9Vnz9CCCHKUOaywZo1qisRi4iKwZote+DRvCcadRqMbfuOIyExWQm9K1p2dg7iEpKwcNoo1K1RhbWPYRicv3YfG3YeVVLvyh6RSIS5q/dh5oo9RSY2At8ev6i4JLz6EIhLd19g+4nr+H3TMYz5bRtajVqC+gMXIjNLckCKFC+rgIE8bS3pB7OlkStgr8aqoa68j6KH3n6sbRNDPXi4SM4gKAv1fPfLxMQEXdo2ZcX+2X0cGZlZMreRmJSC+0+9WbEeHVvKXB8hpPT17NgKPndOYtzQ3hL7srNzsGjlFjTsOgKvfXyV0DtCCCGEEEIIIYQQQoi8bFy+QJzQ1rhuTda++09fY8+xC0hJTVNG16QypF8PXDq8FXq6Oqz4i9c+aN59OIJDI5TUs7KNYRis33EYJpVbQtupERzrdUGT7iMxYMJ8zFr8N/7edgBHz17Fg6evEBgUSitayVlBE75qayl2AtbYBPY1Dfa2NgptrygJCYnw+cgeZ6hft5ZcJny1r5B3v8zNzVGxohNr/9pt+yVWjJTFBc/brO2endvKpf+EEMXQ09PFhmX/w6PLh1HVXXIie9+AL2jefThmLFqBtLR0JfSQEEIIIYQQQgghhJQVZS65cVDvLnCt6FDo/uev32Pa/5bDtkYr9B87G4+eeRdaVtFEIhF8AwLhefMeduw/jlUbd+DQGU94vXqPLm2aoE71yuKyNapWwrW7Xpjz5wZKcASQmZWNQb+sw4YDl+RS3xvfIFy4+0IudZU32QUMJCl6IE8gZCc3qqsp56MoLDoBQeHsGVOb1Kokt1VA+Twe1PjsAbXJI/uztmPjE7Hn+EWZ2/C88xiCH5JFtbQ00bFVI5nrI4Qoh7GRAXau+RW3TvwLZ0fJBOs3H/zRoMtILFy5hS5oIYQQQgghhBBCCCFERf2YDNikHju5MTUtHePn/QWb2p0xfPpi3HzwDMJ84yllScc2zXD77F6Ymhix4v6BQWjWbRi+BIUoqWdlk0AgwMT5yzH3z/VISU2DQCBAaEQUnr56j9OXb2HT7mNYsHwjhk37Fa36jEelJj2h69wY5h6tUavdQHQdOhWT5i/Hw6fKGxtXdQVN+KqlqbgxUYZhkJPLXrUgf0JwafJ69oI14bCmpgYa1Ksjl7r1dHVZ23Vqs+sNj4zGoVOyj4cCQEhYBF6//8SK9ezcllOdhJDS0bh+Lby6dRq/z5kMdXV11j6GYbBl9xFUb9kL1+88UlIPCSGEEEIIIYQQQoiylbnkRn19Pby6cxb/LP8f6tSoWmi5nJxcnLl8Ey16jECH/uOUkuR48rwnjp+9gufe7xAZHcMaDODx+ejWrhmaN6wNG0tzvPsYIN5X3hMc4xJT0GHsEpy58URi35BuLTCwc1M0r1sFzvZW0NLUKHG9k/7chUl/7sSC9Yexctc5/Hv8Oo5cfogrD7zx+LUvfAJCEBYVj7SMrGJXiixPUtMyJGIaGuoFlJQftXzJg0KRcp6PR/lWbTTQ1UYtd0e51c8wjMR9q1nVDZ3yJR+u3X5E5tlKL15/wNpu16wB9PV0CylNCCnr2jSrj3e3T+CXicMkEq2FQiFWb9mPWp1G4tHzt0rqISGEEEIIIYQQQgghRB4c7Gzg5iI5JpGZlY0j566h45DpqNioJxat2ga/wGAl9LBgWVnZCAgMwu0HT/HRPxDD+/eAoYE+q4xHZVc07jIUPp8CCqmlfElNS0ePkbOw+8g5qY9NSEzGu48BuHrnMXYeOo2Wvcfg8fPXCujlz6+giQO1FDjha/7JXgFAXcFjsIVJTU3D67fvWbF6tWtBR0c+yZb5k5UsrazQonE9Vmz15j2cErYvXL3D2jYxNkLzRnVlro8QUrq0tDTx54JpeHnzJOrV8pDYHxwagc6DJmL09EVISEwq/Q4SQgghhBBCCCGEEKVSztnzYhjo62HG+GGYMX4Y3n/0x4GTF3Dk9GVEx8YXWP7Wg6e49eAp2jZvhD/mTUGzUjiJnZ6eAd+AL0UX4vHQumk9GBvq49LNh6xdc/7cAACYPWGIorpYJgWGRKHrxGUICI5kxdXV1bBr6RSM6NmKFWcYBvFJqQiPTkB4TPy3f6PjsezfUxJ1p6ZnYtfp2yXqh76uNrq3qosxvZqjVb3KclupTxVkZWXj6esPuOP1CncevcTztx9Z+7W1NMHj8Qo5Wj7U1dVY27kCkULbK0hMfDL8gtivw0Y1XSX6xoVAKJm0qaGuhoXTRuHavafiWGhENI6cu47RA7tJVX92dg6u3vVixXp2ailbZwkhZYaujjbWLp6FAd3bYdzcZfDxDWTt9/8Sghb9JmPKyL5YuWASDPT1lNRTQgghhBBCCCGEEEKIrHg8HnasWYzBk+cjKqbgMdCwyBis2rIfq7bsR/OGtfD7rHFo26y+wsdx8hMIBHj45AU++QciNj7h2ySiom9JSkaGBhg7tA8On76M2LgEODlUwI27jwEAbfuOwe0ze1GtSqVS7W9ZEhkdi27DZ+K1j6/c6rz18BmaNqgtt/rKi+wc9sqNGhrqCh0jLmhy1/wTwJaWJ89esBIL1dTU0LhhfbnVr6Ymeb/mTxuDB09eirc/fw3B6Us3MLBXZ5nauHiNfR1Atw4tJZIqCSFlXw0Pd3h5HsXGnYfx+6pNEonnB05cwLU7j7Bl1a/o2629knpJCCGEEEIIIYQQQkpbmT/bW72qG9YumYdVv83GjXteOHDiAi5cu1PgKme3Hz7F7YffkhwXz5ui0Jn6cgWCAuNqfD5srMzhUMEa9rZWsLe1gr6eLtZsO4j//bWZVXbOnxvAMAx+mThUYf0sS5699UfPqSsQm5DCihvq6+L0xnlo26iGxDE8Hg/mJoYwNzFEzcpO4nhmVg7W7rsgc1/SMrJwzPMxjnk+hrOdJUb3bIGRPZqhgpWpzHWWVQKBAK/e++OOlzfueHnj8cv3yMrOKbS8kaF+ofvkRTPfQFNGdsHvJ0V69Nqfta2jpYm6VSvKtY30Au6XpoYamjesheYNauHh8zfi+KqtBzCiX2eoqZU8ufLO4xdIS89beZPH46F7++ac+kwIKTsa1K6Gl1cPY9XW/fhr4x7k5rI/U7YdOIPLtx7h2qENqOzqpJxOEkIIIYQQQgghhBBCZNayST18fXIBV24/xsHTV+B55zEEgoJXNnv47A06DJ6GJvVq4I9fxqNd8walluR49+FTPH7+qtD9Bvr6GD2oFy7fuI+P/nmTtcXGJZTrBMcPfoHoOmw6QsKjWHFtbS1sWjYf5qbGCI+K+XaL/P5vLMKjYljjP/n9uXY7/tl5GEYGBjA2MoCxoQGMDPW//WtkACMDfXHc0twULRrVgZGhoaLvbpkWFhEFz9uPWDFtLS2FtqlRQMJfYe9vRcrMzMTL129YsVo1qsHAQH5jwoJcyTHRTm2aoW7Nqnj1w0S7KzfuwoCenaT+7EpMSsa9xy9YsZ6d28rWWUKI0qmrq2POlFHo2ak1Jsz5Q+L9HR0bj/5jf0HvLm2xZdWvsLGyUFJPCSGEEEIIIYQQQkhpKfPJjd+pq6ujS7sW6NKuBRKTknHgxEX8vXUvIqNjJcqWRpJjQbM4Du3XA05WBtDQkHxY508ZAQASCY5zl/4DAD91giPDMDh36xlGLPgHmVnspDo7azNc/vdXVHdzlKrO8f3bY8fJG0hNz+Tcvy9hMfh962n88e8ZdGpaA2N6t0S35rUKfB5ViVAoxIqth7F+10kkp6aV+Li+XVorsFffWJoaICQqbxbi0Lh0hbf5o8SUdHz4HMaK1a/mDC1NDbm2ExbHftw1NdRhqKcDAFg4bSQejngj3hfwNRRnPO9iQPd2Ja7//LX7rO3GdavDysJM9g4TQsocTU0NLJ49Hn27tMG4Ocvw7LUPa39IeDQWrNiGC3vXKKmHhBBCCCGEEEIIIYQQLrS0NNGnS2v06dIaMXEJOHr+Og6euoI3H/wLLO/18h06Dpmel+TYqrnCkxxDwiOKLWNfwQa/z52M1Zt2480PqxSW1wTHu49foM/YOUhOYY8VmZkY48L+DWhSv2aRx6ekpiE8KgZ//bMHR89dldifnJKG5JQ0hIRHFtsXHW1t9OvWDmOH9EHzRnVKfeVPZYiNS8A9r5e48+gZ7jx6joAvwRJltLU0FdoHNTU18Hk8iJi8FRwLm0BZkZ6/9GZNHM3j8dC0UQO5tpH/fqmrqYHP52PhzPHoN2a2OP7uoz+u3LyPbh1aSVW/560HrJUntbQ00aFVU059JoQon6uzI26d2Ys9R85g3pK1SMl3Xcs5z9u4+/g51i6Zh9GDe5WL7y9CCCGEEEIIIYSQ8kols7dMjI0wa+JwTBo5ALsOn8aqTbuLTXL89+/FcHWWLoGuKAWdMqtgYwUNtcJXxCs2wXFUd3l1T+kYhsFb3yCcvu6F09e9EBAsObBW090Jl7b/igqW0q+W6Opog9C7u3DL6zXik1KRnJqB5NQMJKWmIyk1A8lpGUhOyfs7KTUdKWlFJ0KKRAw8H76F58O3sDQ1xIjuzTCmd0u4O9lI3T9ly8nJxag5K3D80p0SlVdXV0Oj2tXQr2trTBnRR8G9A+ytzfDyY5B4OzS25MmX8uD1JoA1kKipoY6G1V3k3k5oLDtp097KVJwY3bFVI9Su5obXPnkXJ6zccgD9u7Ut0Ul5kUiESzcfsGI9OraUQ68JIWWRh7sLHl3Yg817T+C31duQkZkl3vfw+VswDEMDeoQQQgghhBBCCCGEqDhLc1PMGjcYs8YNxtuP/jhw6gqOnruOmLgEibLfkxwb16uJP+ZMQvuWjRV2jpD5YUwFANTV1GFrawmHCjZwsLOBnY0VdHW/Te7YpV0LdB08CY+eeYvLl7cEx6Nnr2L07D+Qm281OxcnO3ge3oJKzg7F1mFooA9DA3307NSqwORGaWRmZeHQ6cs4dPoyKjk7YMzg3hg5oAesLc051VuWCAQCXLvzGLcfPcPdR8/x7mPBycE/crSzVXi/dLQ1kZ6ZLd5OSEyCna21wtv9LicnB89eeLNi1apWhqmpiVzbSUhKZm1/Txzt1bktqrg545P/F/G+Ff/sQtf2LaX6vLrgeZu13b5lE+jr63HoMSGkrODz+Rg/vD+6tGuBKfOX4tL1e6z9ScmpGDd7MQKDQvHXohnK6SQhhBBCCCGEEEIIUTiVTG78TltbC9PHDcX4Yf2KTXJs3GUIzu3fhGZyWsWxoJUb8w/sFWT+lBHg8XhYsHwTKz536T+AIAu/jOsvl/4pA8MwePPp638JjU/wOaTwmULbN62FkxvmwlBfFyjB41YQAz0d9G5b8lklhUIRUtMzEZ+cisv3XmHv2bvw+RxaYNmYhBSsPeCJtQc80ay2G1bMGICmtd1k6mdpy8jMQv/Ji3H13rNCy/B4PNSt7o7WTeqiTdO6aFqvBvT1dEutjw7W7NUFIxIykCsUQUNN8n0lb2kZWXjjG8SK1aniBF0dLbm3FZJv5UYHm7z7zePxsHDaKAyYtEgce/sxAFfvPkGXNk2Krfv56w+IiolnxXpRciMhPzU1NTXMGj8ErRvVRO1OI8XxpJRUfAkOh4uTnRJ7RwghhBBCCCGEEEIIkaeaVd2w/g83rFo4DQdPX8Ffm/YhOExy7O3Jy7foNHiyQpMc84+LdmrbAvVqFDxuZqCvhyvHtheY4Nimz2jcObvvp01w/BoSjh2HzmDN1v0S+xrWqYaLBzbCwky6pLK+Xdti6uiB2Hf8AmvSO1kFfAnBwr824rdVW9C1XXOMHdIbnds0g7q66l42EBQSjh4jpsPH93OJj9HR0caC6WMU2KtvbC3NEBCct/JpSFgEanhUVni733m/eYeMTPYEwM0aN5R7OyFh7NVdK1h9GxPl8/n434xxGDktbzz06au3uO/1Aq2almycPysrG1dvP2TFenZuy7HHhJCypoKNFc4f3IKTF65hxqIViM03scPGnYfx5/wpKv19RQghhBBCCCGEEEIK91Oc9SlJkmN8QhLa9RuL/ZtXYFDvLpzbLCi5USQSlejYeZOHA4BkguOK7QCgUgmODMPg9ccvOH3tEc7cKDqh8btRvdtg+5JJ0NAo3ZefmhofxoZ6MDbUw8zhXTFjWBc8f/sJe8/dx/FrT5GWUfCA4KPX/mg7fiUO/DURAzs2KtU+SyspORU9xi3EoxfvJfZ5uFVE6ya10aZpfbRsWBsmxoZK6OE39lbs1TqFIgaRCRlwsNBXeNtP332GQJj3XlXj89G4pmIG0SVWbsyX1Nm7U0u4uzjCLzBYHFuxeR86ty7+woPz+WYsrFKpItxc5Lc6LSGk7KpRxRXmpsaIS0gSx7x9/Cm5kRBCCCGEEEIIIYSQn5CmpgbGDemFEf26lijJsUvb5jiybSWMDA3k1gc+jz0uWtyYaGEJjnHxiXkJjm4V5dY/ZfoSHIZTl27h9OWbePXuU4FlendujUObl4tXt5QGn8/H5r8WYOPyBUhLz0BSciqSU9NY/yalpCI55fu/ad/+Tk5FYnIK3nzwh0AgkKhXKBTi4vV7uHj9HmysLDByQA+MGdwLrhWLX1WyLPnoF4iOgyYiPDKm2LImxoZo1aQ+WjdtgB4dW8HBzkbh/bO3sZBIbiwtAoEAXk9fsGJuri6wsrKUe1uh+e6Xg62F+O9BvTrjj9VbERQaLo6t3LirxMmNdx4+RXpGhnibx+OhW4dW3DpMCCmTeDweBvbqjLbNG+GX31fh8OnL4n0ZmZn46BeIGh7uSuwhIYQQQgghhBBCCFGUspvcKMOMoto62pg+fhjGD+//Lclx4y5WkmNOTi6GTJyHL8FhWDhzfKHJQzy+WrFtqamrg5dvII/h8QEN7RL1dd6MSQBfAwuWrmPHV+5Am5bNUMtD+lUCebzi+10ofnaJizIMA+8Pn3HK8wHOXHuIwBIkNALfBjHmjeuHyUO6sh97Rihtb/Ooy77aHg9Aw7o10bBuTaxbOB6nrnth39nbeOztK1E2VyDE0P/9i4R0ASYP6vQtKJIcCCw1BQwaR8cmoPPoBXjzkT0rqa6ONs5uX4oOzet/C3xPzBXlytg4l9UVvz3XOlpqsDQxQExiqnhPUHSqwpMbs7Jz8PLDF1ashrsDDPWlH0wuTlJaNpLSc1gxB0sDQJj3XlMDsGDSYIyZt0oc83r5Hg+8nqNlo1rfAhoFz+B78QZ7htIendoAagW8H3gcni9Or3HZVmTNw2FWaTnPSC0VGVeiVT6OjxlTsuR+uR/LlTJfKxy+s3lqGqhbvTKu338qjr3y8Uf/Hh2KP5jz483hMSvhJBBlEpf3tlI/F2R/zEuyIrnCcPpMUebjraKPGee2udxvFe0359eZit5vpbarot8hnN6bqvq7Esr7bajMz0IRx+dLWX1X6veHMp8vjudzuPw/QljAuRihEs8vEUIIIUSl5E9yXLF5H4JCJcfoPG8/RPOeo3D50Ba5JW/ln/S1JBO+FpvgeHoXqlVWzRUcA4NCxQmN3u8lxxV/NH3sYKxf8gvU1DiM4+Lbc2BooA9DA+nG0mLiEnDo9GXsOXIOvp+/FlgmMjoWqzbvwarNezBmSG9sXbEIWlqanPpbGl689kGXoVMQ/8NEgD/S09VBi0Z10bpZA7Rp1gA1Pdw5Pw/ScrA1Z22XZnLju/cfkJKayoo1byr/iXwZhkFwaP7kxrwESg0NDcybNhpTFywXx27ef4IXr9+jfu3qxdZ//upt1naT+rVhZWleSGlCyM/A3MwEB7euxMNn3qzPl1fvPlJyIyGEEEIIIYQQQshPikuWUpn1fSVHvydX0LV9S4n9v63YiPG//IHcXFmTu7it3PjdvGljsXrxHFaMYRi89vGTuV+K9ME/CP9bsweV2oxG/V7TsGbnyWITGx0rWGLOmD54cmo9vtzZhylDuxW7Ip0y6OvpYHSftnhweAV8Lm3CnNE9YWHKXtmQYRhMW7oTy/89qdwL/gsQHB6FFgNnSiQ2Ghvq4+ahtXmJjWWIgw17FcNn/rGFlJSfFx++IjuHfdHgO78QbDl6A4cuPcLl+6/xyNsPHz6HISImERmZ2TI/1/nvj5amOqxMJWdJHtqrPRwqWLFiK7ceFv8tFArxJTgMV28/xMZdhzFlwXK07TsWvgHsAehenVvL1E9CiGqqU6Mya7u4C2gIIYQQQgghhBBCCCE/h+9Jjr73T2PnmkVwsreVKOPj+xmNug7Dq7cf5dImn88e22NKOCHL9wTHZg3rsOJx8Ylo03c8vgSHyaV/pSEwKBSrNu9DvY5DUKlJTyxaubnI87I8Hg/rlvyCjcvmlXpC3Y8szU0xZ9IIfHhwDo8uHsDowb2gV8QKknuPnkOPEdORlp5RaJmy4O6j52jbb5xEYmMlZ0f8OX8qHl06iAS/R7hydBvmThmFOjWqKuV5cLCxYG2npKYhKTlF4e2KRCI8fvqcFXNysIe9XQW5t5WQmMxaWRGQvN+jB/WGlQV7bHjVpt3F1i0SiXDp2l1WrEfnNjL2lBCiaurWqMrafvVOPr9rCCGEEEIIIYQQQkjZU3ZXbpQDfX09nDuwCbN+W4Vte4+x9u09cgYhoRE4tXcDjAwlE46KI4/kRuBbguOWPUcQGh4ljpkYSd8fRYpLSMaidfuw5+S1EiV6OVWwQr9OzdCvUzPUq16pTCYzFqWKix3WzBuJ5TOHYN7fB7DliCdr/x+bjyMuMRXr5w8v8HVQ2nwDQ9BhxFyERbKT6awtTHH9wN+oXtlZST0rWp3KDnj5MUi8HRyThuCYNDhaKm71xuCIOImYUCRCfHIa4pPTCjxGS1MdRga6sLM0RfM67jA21Cu2HZGIwaOPUaxYLTe7Al8vGhrqmDthEGb8sVEcu/HwBbqP+R++hkbic3A4cnKKTsS2sbJA/VrViu0XIeTnUbd6Fdb2q3e+YBhG5b5zCSGEEEIIIYQQQggpV/j8bzcWLZmq0tTRwrjhAzFycF8cPHUJi/7aiNj4RPH+qJg4tOw9Gke3r0aPjoVNkFiyJMX8yY0ikQjga5ToWANDY1w5vhtdB4/Ho6evxPG4hESs2roPO9f9WaJ62B0QSn/Md1JMavn5awhOXbqJ05du4LVPySaYMzMxRu8ubTBxeF+JpAxOeNzGJHk8oEmD2mjSoDb+WbYAJy5cw96j5/D01TuJsjcfPEW7/uNx5cg2mJkac2pX3LisCni+Lly7i0ET5yE7O4cVb1S3Bi4f3gpTEyPZ25OX//ptYWoEbU0NZP0w1hcQGIT6dWootPlPvv6IT0hkxSrY2iA6OgbGxkbQ0pLtc6cgAYHsCVl1tbVgZqTHep9qa6rjl0kjsGDZBnHsnOdtfPzkj6ruLgAAngY76VYoFOLGPS9Ex7LHd3t36wSeWr6VRXncElcZUU7xhRSB4/saUOKYkLLGozi3q6LjaJzuN9fXmbIm/uahTk0PnL1ySxzxfvtJive79NdvyedYUuqUNj5eHj9PCCkhri+zMrboBCESOL3Gy+nrm8v7ujx+dXH9HORzSA3h8v/Ugs4Vqhc+wRYhhBCS30+d3AgA6urq2LzyV7g42WPuH3+zkvNuPXiCZt2G4eap3bC2siiiFkkFJQ/ExiXA1FBXqnoYhkF0bDwrZmNpXuQxAoEAnne8wOfz0Ll1E4XNMikUCrHjmCd+37AfiYUkfn1X0e57QmNz1K3m+lMkV2hqauCfRWNhbmKIJVuOs/ZtPnwF8YnJ2Lt8CjQ0lPc2evnOD13GLEBcQjIrXtHeBjcO/g0XR/nPvikv1VwrwMRQF4kpeTN5PvoYBUdLV4W1aW5igMDQaKmOyc4RICY+BTHxKYiMS8L4vq2LfX37hCQiMY09CNayTqVCy48d2BXLNx9ETFzeIOOVO09K3MfeXdqWiURbQkjpqZtv5cbE5BQEhUagokPZ/dwnhBBCCCGEEEIIIYTIn4aGBsYO6YM2TRug27Bp+BTwRbwvIzMLvUfNwoZl8zFj3FCZ28g/LiLthK8GBvq4cmwX2vUZiRev34vjCflW3SsLBAIB9hw9hx2HTuGNj1+JjjE3NUbvLm3Rv1s7tGpSD+rqZXsI3kBfD+OG9sW4oX3xwfcz9hw7h12HTiM9I1Nc5vlrH7ToOQrXT2yHna21EnvLdvDkRYyd/QeEQnaCa/uWjXFm7wbo60k3Vq9ofB4PFe2s8OlL3iqlD5++UHhy47MXryRij58+F6/mqKujAxMTYxgbGcLE2BjGxkYwMTYS/y3NuOPDpy9Z2xXtrAocS500oj9WbtqNpORUcWzV5j3YsHQ+/L8Ewz84Ev6BQfAL+AL/wK8I+BIkkcBaxc0VlVwqlrhvhBDVVreGB2v77Uc/CASCMv89SwghhBBCCCGEEEKkVy7O+PB4PMyeNBJO9hUwbMoCZGZmifd98P2MBUvX48DWlVLXyefzWYN3J85dQbMGNdGySf0SJxwmJiVLrMoWHB4JS3MTVLC2hKYmeyaDmLgE9BozF0+9fQAALRrWxrk9f8PE2FCq/hfn0UsfzPhzG958Ciy0jLO9Dfp1bo7+XVqgdmUnhSc0JiSlIiA4AgHBEQiJiIWDrQV6tG0MQ33FDVLxeDz8PmUAzE0MMH35blZy7NErj5CYko6T63+Bro78ZrcsqXtP36DH+EVIS89kxT3cnHD9wN+wtSo6SVbZ1Ph8NKtVCZcevBXHXgbEoXdjJ+hqKeajqVW9ylDj8xAQHIWk1AzkCqSb2TcyNgnR8cmwNjcustzDD+xVGx2sTeFoY1ZoeR1tLcweOwALV++Qqj8AUK2yK/6YM0nq4wghqs2hgjXMTIwQn5iX3P7qnS8lNxJCCCGEEEIIIYQQUk5VdLTDo0sH0W/sbNx9/EIcZxgGs35bjcCgUKz/c55Mk6bmT3SSNrkR+JbgWL2qOyu50aqYCV9L26Nn3pj+6yq8/VB8UqOFmQl6d2mD/t3ao2XjuiqbaOFR2RXr/5yHIb27oMuQKYj7YbW/TwFf0Kz7SFw/sR3urspPKNu46zBm/75GIt6vW3sc2roSWlqaBRylfI1rV2YlN37+EoyIyGjY2lgprM2ExKQi92dkZiIjMxPhEZES+0xNjNGvdw/Y2hSf1BoaHomvwaGsWOPalQssa6Cvh+ljh2DZ+rzx0MNnruDwmSvFtvNdr67tS1yWEKL66tZkJzdmZmbBN+ArqlUpfGJpQgghhBBCCCGEEKKaVHOURUa9u7bD3XP70GPYNMTE5a2W+Pj5a6nr4vF4qOhoh8CvIeIYwzB48OQlvgSHoU/XdjA1MS62nsjoWInYoMm/iv+2sjCFva0V7G2sYGdrhcu3HuJrSIR4/4Nnr9Gi7wR4HtoIewsDqe+HRH9i4rFg9R4cvnC7wP16utqYPLQ7BnVtidoeeSs0MkIB57YBIDUtAwHBkQgIjsDn4Aj4B0Xgc9C3hMb4pFSJ8hame7FkxlCM698R6uqKWcESACYP7gxjQ32MWrgJgh8S4q4+fI1OE5bj4tb/wdhQT2Ht53fx1mMMnPYnsvMlxjasVQVX9q6CqZyTXRWlcQ0XeD56D+F/g+C5QhEefYxCh9p2CmlPW0sT7RtXR/vG1cEwDNIzs5GYko6k1Az2vynpSEnLhKiA5d0/h0QXmdwYmZCBT6FJrFiL2sWvRjl5WE9sOXAW4VGSnwkAYGxkAHcXJ7i7OKGSi+O3v12dUK1ypZ9ipVRCiHR4PB7q1qiMG/efiWOv3vuiX7e2SuwVIYQQQgghhBBCCCFEmUyMDXH12HZMnPsnDpy8yNq3efdRJCenYv/mv6SuN39y4wffz/Bwd4W5malU9UTHxLG2bawsiiwfn5CEpev+xZegUIwd2he9uijm/GdkdCwWLNtQbJKVpbmpOKGxRaM6KpvQWJB6tTzw8OJ+dBg4EaHheZN4hoRHonmPUbh67F/UrVlVKX1jGAZ/rv0XS9dtl9g3Zkhv7Ph7sUxJu6WlZuWKMNDTQeoPE9Y+ePICg/p0U1ibtWpUw6Mnz4ovWICExCR4Xr+JcaOGF1v2gddz1raRgS5quDkWWn7G2CFYv/0ga5XQklJXV8fwAb2kPo4QorrMzUzgYGeDkLC8ROxXbz9QciMhhBBCCCGEEELIT+jnGXEpoQZ1auDs/o1o1m2YOPY1JAwZGZnQ1dWRqq4+3Tri1AVPBIWEs+JhEVHYfuAE+nRtj8qVnIuso7gT99GxCYiOTcDLt58KLfPB7wua9BiLq/tXoZp70e0VJjdXgE0HzmPp5iNITc8osMzg7q2wZsF4VLCW/yyqr3w+Y+yijXjvHyzVcbEJyZi6ZBu2HLqE1fPHoEvLegpL9BrctTlMDPXQb+YaZGbliOOPX/uh6dDfcGztLNRwL3ywRl4Onb+DsYv+gVDInhW3XdO6OLt9GfT1pHsdK5ORvg5qudvj1ae85/3aqzDUdjaHhZG2Qtvm8XjQ19WGvq427K0lV1UUiURITsvE7Wcf8OFz3myqgWExaFbHvcA6RSIGR++zVzvV1dZE3SoOxfbH0EAPt46sx/rdJxGXkARXJzu4u9jDzb0q3F2cYG5mQkmMhBCWOtWrsJIbvd/7KrE3hBBCCCGEEEIIIYSQskBTUwN7Ny6Ds5M9/lizlbXv4KlL+HP+VDja20pVp462Fms7Ji4eOw6cQKc2zVGnpkeJxy/yT/p69dZDZGZlw97WGna2VrC3tYZ9BWuYGBvh89cQdB08CZ//m2j2ys37WDJ/Kn6fMxnyGi3Jzc3Fpj1H8efa7UgrZHzU0twUfbq2Q//u7dGiUV2o8RUzViMSiRARFQv/L8EI+BqCgC8hCPgSjMDgMFhZmGFgr84YM7iXQhMq3V0r4tHFg+g4aCJ8A76K43EJiWjTdyzO79+I1s0aKKz9gohEIsz6fQ227DkqsW/O5JFYs/iXMj9+pq6mhqZ1quDaQ29x7NmrN+jVpT2087235KVt6xZwruiI4JAwJCYlISkpGYlJyUhNSyvR8WHhkUhNTYOBgX6hZTIyM/HC+x0r1rRO1SITTc1MjTFxRH+s336wZHcEgOF/q77Omz4eVdyLn1CWEPJzqVvDg53c+O4DRg7qpbwOEUIIIYQQQgghhBCFKHfJjQBQq1pl8Hg8MP+tysYwDPwCg1C7ehWp6tHT08WIQX3g9dwbdx48gUiUl2yWk5OLM5dvYMb44TDQL3xFvypuLrC2NEdUvplKpRUeFYMWA2bg/M6/0KJhzRIfJxKJcMfLGzOXbMKnwJACy1Rzc8LmP6aiZcManPpYmIzMbPSdvgKhkbI/Bp8CQ9Fj4p9o27gm/v7fWNSsLFuSZ3E6Na+Dm3v/RPdJfyExJW/wx/drOBoNXoT1C0Zi4oD2ChtE23LoEmYu3yER79OxOY788xu0tDQV0q4italfmZXcmCMQ4ci9z5jRwwN8JQ5G8vl8mBjqoZqrHSu5MTQyHjm5AmhqSH583nkXga/R7BVGm9VyLbBsQdxdHLBj5Vx2UNtE+s4TQsqFutUrs7ZfvfsEhmHK/IUchBBCCCGEEEIIIYQQxeLxePj9l4lwdrTD6Jm/QyAQiPe9++QvdXJjnRoeeOvji9wf6snNzcWl63fw+WswundsU6JJZKNi2MmNT16+wZOXbyTK6erqgGEYZGZmseJL1mxFeGQMtq5cyDnJ79aDp5j522p8CvhS4P7qVSph9W+z0b5lI3ayFiMqsLw0gsMicOfRC/h/CcbnryHw/xKCz19DkZmVVWD5j/5fcPfxC2zefQR//zEHndo049yHwthXsMaD8/vRdehUvHjjI46npqWj85DJOLJtFfp2a6+w9n+Um5uLcbMW49DpyxL7/lo0A/+bPlZlzoc3q1MV1x+9Fl+fkJ2dgzsPn6BL+1YKaY/H48G5ohOcKzqx4rm5uUhOTvmW8JicgsTEJPHf0TGxrOsdAr98Ra2a1Qtt486DJ8jJzRVv83k8NK1T/PUW/5s2Bpdv3od/YN74sLq6OlwqOsDNpSLcXSvC3dUZbv/9a2lhpjLPMyFE/urUrIpznrfE295vPyqxN4QQQgghhBBCCCFEUcplcqOurg4qOtjhS3CoOPbRL1Dq5EbgWwJU04Z1kZiUjFc/DPAA31ZDTE1LLzK50UBfD3fPH8C2fcfg6xeA0IhohEZEF7uiY72aVZCWngnfz0HiWFJKGjqMmIvDG35Fvy6txHGGYRATl4iAoDD4fw2D/9dQfA4KF/+blZ0j2QAAQ31dLJ09ElOGdoe6euEzLHK1ZteZEic2qqnxUdHOCsYGenjp81li/+0nb1G310yM7N0Wy2YNh62V5Ip8XDWu5Y57h5ah07iliIxNFMezc3Ixddlu3H76HjuXTISJUeEzWUqLYRgs33ocSzYfkdg3ql8n7FwxV6HPkSI52ZqjRZ1KeOAdII59jkzBA58otKpuo8SefVOxggX4PB5E/w02CkUifA2PhbsTu29RiRm4/IKdIGxhoo/OTaqVWl8JIeVL3Rrs5MaEpBQEh0XCScoLkwghhBBCCCGEEEIIIT+noX27YuehU3j4NG/FuI9+X9C9Qyup6rG1scLYYf1x5tJ1xMYnsPZ98g9EZHQsJowYWGyCY0lXqcsoYpx016FTiI6JxdF/V5cooTK/kLBIzP1zHU5fvlngfiNDAyydPwWTRw5QyCqJF67dRd9xc1lJZCX1wS8QXYZMQYdWTbBm8S+oUdVN7v0DAHMzE9w6vQt9x8zGrQdPxfGcnFz0HzcHv0wagRWLZkJTU0Mh7QNAVlY2Bk2Yi4vX77HiPB4PW1f9ikkjByisbUUwMdJHDTdHvPULEseu3rqPWtWqwNbGqtT6oaGhAXNzM5ibS46hnzh9Dp/88sZrA78GFZrcGBoeiWu3H7BiNas4w9ig8OsivjM3M4HXpUN48uotGIaBu4sTnOxtoalPE74SQiTVrenB2n7zwRdCobDIVWIJIYQQQgghhBBCiOopl8mNAFDV3Zmd3OgfKFM9DMPg/JUbeOvjK7HPvoINrC3Ni63D3bUiNv61CMhJE9eZlJyK0IhohEXGIDTyW8JjWEQ04hKSUbu6O/43dSSysrPRc/RceL18J64rJycXA6f9iXGDXiEtPQP+X0MREBSOlNR0qe7XqH4dsHLuGFiZK3YQITg8Bn/vOcuK8Xg8ONhYwNXJBm5OFVDJ0QaujrZwc6oApwqW0PhvFby7T99h3pp9eP2R/dwxDIP9Z2/h5NWHmDu2D+aO7Qs9XW259rtaJUc8OrwM/Wevg/fHr6x9Z28+w6sPX3BkzUw0rsV9UFEkEmHOyt3YdPCixL7ZY/tj7aLJKj9bZc9WtfEhMBLxyXmrYV58FgwPBxNYGMn3uZOWlqYG7G3MEByRl4AbGBrNSm4UiRgcuRcIgZARx3gAhnVp9N+qjdxn8SWEkPwc7WxgamyIhKQUccz7vR8lNxJCCCGEEEIIIYQQQsSquLmwkhs/yTgmam1lgQkjB+LG3Ud48fo9a19ScgqevXqL1s0bFVnH3KnjMHX+EvHqdbK6eP0e2g+YgIsHN8PM1LjIsrm5ufgaEg7/wGA8efUW/+w4XOgKiaMG9cSqX2fCsoDEL3lITknFxPnLZUps/NGNe1649eApRg3qiWULpsHGykJOPcxjoK+HS4e2YPjUhRKJoOu3H8SDJ69wbMcauDjZy73tlNQ09BoxA/e8XrDi6urqOLjlLwzq1VnubZaGTs3r4p1/sPj1LxQKcfDEOcybPr5MJOm4OFdkJzd+CYJIJAKfz2eVEwgEOHTiHOt1zOfx0LF5nRK3ZWpihK7tWnDvNCHkp1enelXWdkZGJnwDvsCjciUl9YgQQgghhBBCCCGEKEK5TG7Mzc1FaHgUKxYSGiFTXe8/+hWY2Ojm4oQ+XdtLnOwvCR6PBxNjQ5gYG6JG1cJPyOnp6uDmsS0YMu03XLieNzMiwzDYdeyS1O0CQB0PV2xZMg2Naku/iqUs5q3ezVo5Uk2Nj1dn/0F1d6dij23dqAaen9mAwxfv4rf1BxEeHc/an5GZjaVbjmHXietYOmsYRvZuK9eBIacKlnh0eDkWbjiCjYc8WfuCI2LRcuRiLJs+CPPG9JDpdQAAAoEQ43/bhIPnbkvsWzZnLBZNGaryiY0AoK2pgWFdGmLjsbz7mSMQYdd1X8zqWQ26Wsr9qHK1t2InN4ZEi/9mGAZnvL7ia3Qq65hW9dzhamdZan0khJQ/PB4PdWtUwc0Hz8SxV+8/oU+X1krsFSGEEEIIIYQQQgghpCyp6ubM2v7gJ1tyIwCoqalBU6Pg1fpKsorfpNGD0altczz3fofQsHCERkQiLDwKoRFRCA2PQnRsvETiY9f2LTGwV2eM/2Uxsn8YU3zy8i2a9RiBq0f/hYOdDULDo+D/JRgBX4K//RsYjICvIfgaEg6hUFhkv+rWqIrNKxaiUd0aJXgUZLdswy7ExCUUWUZXRxuVKjrAzcURrhUdYGtlgd1HzuLtR39WOZFIhL1Hz+HE+WuYN3U05kwaAT09Xbn2V0tLE8d2rIHJ//7CrkOnWftevv2AOu0GYMfaxXJNNoyLT0SXIZPx8s0HVlxbWwund69Hl3bN5dZWaXOwtUD7JrVw4/FrcSwkLAI37j5C53Ytldizb1ydK7K2MzIzERkVjQq2Nqz4tdsPEBbBvtaiQ/M6sLexADgm7hJCSH6WFmaws7Vmfe68evuRkhsJIYQQQgghhBBCfjLlMrlx9eY9ePvBjxWrXUO2ZL6k5BSJWKumDdCySf1SSTrT0dHGqR2rMO23v7Hz8DmZ67G3scCvU4dgbP+OpTYz5N2nb3Hm+mNWbNKgziVKbPyOz+djRK+26NexKTbsO4/Vu04jPYM922pkbALG/7oJmw9exO4Vs1C3mqs8ug/g26p+6xeMQuuG1TDm121I+GHlQaFQhEX/HMXtp+9xcNV0WJsbS1V3VnYOBs9eg4u3n7LiPB4Pm5fMwJThveRwD8oON0drtKhTCQ+882YEjUjIwL+enzCtW1VoaShvxlIXeyvcfpY3iJqQko6E5DSYGunjystQ3PdhD+BZmhigR4uapd1NQkg5VKd6ZVZyo/c7yQkXCCGEEEIIIYQQQggh5Velig6s7U8BXwpcja0kLnjewtsPkucgK9hYoUGdkiUGOjnYwcnBDhDlSuzLyclBRFQsQiOiEB4ZDUtzU7Ru1hA8Hg+O9rboOXwaa2zW73MQarTuC4FQiKysbKnvj5mJMVYsmoExg3spfHzU73MQNu05xopVq+yKTq2bopKzw7eERmcH2FhZSIwxTxrRD4fOXMVvqzYjIiqGtS89IxNL/t6GnYdOY/n/pmN4/25yvS9qamrYvuZ3VK9cCXP/XIucnLznLTUtHUMmLcCtB0+xcdkCzsmVYRFR6DBgAnwDvrLihgb6uHRoM5o3qsup/rKgS8u6eOcfhKjYRHHM8+Y9VK/qDjtbayX2DDA2NoK5mSni4vMScAO/fGUlN4aEReDa7Qes42wtTdG5Rb1S6ychpPypW6MqO7nx3QeMGNhTiT0ihBBCCCGEEEIIIfIm23JyKuz9R38sW/cvK+ZR2RVTxwyRqT4LM1PWtpaWZqklNn6nrq6Of1f+D3/OHlNkOS1NDVSt5IReHZph3sTB2LlyLu4d34jwZ2cQ9OAQJgzqUmqJjQKBELP+2sGKmRkbYMl02Z4HXR1t/DplEPxv7MK4AR0LHJB95xeEdiMXISgsuoAauOneqh5en/kbLepJJsnefvoe1XrMxsQlO7Dz1C2cvOaFG4/f4tlbP/h9CUN0XCJr9UoASE3LQLfxSyQSG9XV1XDw7zk/XWLjdz1b1YaliQEr9jU6FduufERmtkBJvQKszY2gr6vFin0Oicbl5yG49iqMFefzeRjWpRE0Ncpl7jghpJTVrV6Ztf3qva/EzOaEEEIIIYQQQgghhJDyiWEY7My34l52Ti5EMqyulpySWmBiY+3qVTF6SF9oFLKiozQ0NTXh5FABzRvVxaDeXdCmeSPxmGvzRnXx8NIhiQSwtPQMqRMb+Xw+Jo8aCL/HFzF+WF+Fj48yDIPZf/wNgSBvrEtTUwNn96zDmt9nYfzQPmjVpB5srS0LHGNWU1PDqEE94ff4IpbMmwI9XR2JMhFRMRgz63fU7zgYtx8+ldjPBY/Hw7Sxg/HU8wjcXBwl9u89eg4NOg3B+0/+BRxdMgFfgtGs+wiJxEYLMxPcPbvnp0hsBAANdXUM79GK9TwLhUJs3X0IcfGJRRxZOlwqOrG2PwfmPR8xcfHYtucw6/ODz+NheK82UFdX3iS1hJCfX52aHqxt77cfldQTQgghhBBCCCGEEKIoZTf7hsch77KQi/pFIhHGzPwNubl5A0dqamrYt2kFtLTyEpcYkbDETdlaW4Bh8k7gZ+eIEJeYAgtz0yKOKoSmnvTH/IcH4Pd50+HhURXbD5yCuroaKjk7wu2/WyVnB9jbWhU+OCfIlLltAODlZkhVfuexC/DxD2bF/pw5HKYmRtK2zNqytjDFjmUzMH14D8xfsxfXH75i7U9Jy8DCdQdwbMOCbwE1dsKaVC3ne77snYxw+8g6LNtyFMu3HmUN7CSlpGP36dvYffp2ofVpaWrA2FAfxoZ6SM/IQlhUHGu/tpYmTm7+FV1bNwREXBL9lJnTXHTb2mrA1H7Nsf7YXSSn5b0mA6NSsfHSB0zpUgWGupqK7qQEHo8HZzsrvPMPAfBtEPrIbR8ItS3Y5QCM6t4MLg75ZlbVMJS9cTWO97eAmY9LjkuCFMcEb04J4qWXXC5XpZgUL0lFk+E4P2YcPg95SnzMfviNVLc2ezb0uIQkhEYlwsHOJv9R/x3L8TETcvlMKfnvqwJx+u5TVdJfZPYjpSa6Mhz6rtQEXRV9zDi1y/U+K6nfALe+c7rfKnqf5XG8zFT5MVNi37mgyQ6kJ+LymaLM1ziX71yO/eb0Ocy1bQ7HcznnCHC73wX9pmXK4+9cQgghhJSW4+ev4sK1u6zYkN6doa4u/RBxQWcW27VsgqYN65bapK8elV3h5XkEXQZNhI/vZ6mOtTAzQSVnR1SvUgkTh/dDrWqViz9ITq7ceohrd71YsdkThsE136qaxdHT08XiOZMwflhfLF6zFfuOnZdIVH3j44v2/Sdg7NA+2L7md7kmbtaqVhkvb5zA9IUrcODkRda+TwFf0LDzUKz/cx4mjugv1WvijY8vOg2ciJi4BFbcwc4GN07sgJuLkzy6X2Y4VbBC+ya1cOPxa3EsOSUVm3buxy9TxsLYiMO4IkeuLhXx7KW3eDssIhJZWVnIyMrG5h0HkJKaxirfoXkdONhalnY3CSHlTN2aVVnbr30+QSgUltrk7YQQQgghhBBCCCFE8XhMKV71HB0djRYtWkjE75/fCysLM/k1VMhd8nr+Gs26DWXFRg3qhb2bVrAL8qUb0Fu/bS/rRH6vLu1Qq7rkCn7F4nrRviCn+DKFHsstuRFSJDfGJybDre0oJCanimM1K1fEi7P/yHDyseiBsRuPvDFv9W6JRMpHx9eicW0ZnqMfW1Yr/HVy7+lbDJ61AtFxSQCA6m5OeO8fJHNbhvq6uLhzKZrXr/atbR6Hk7QFrGpZekrWdmRcMjYcv4v0TPZrWl9bHQObO6O2i7kiOlek9wGhOHvrBdIyc/EuKAEpmQLUqFGDNRPx4E4N0ay2m+TBGrInLkNd9gRcANySG6VI9JakxORGrheqcj1e5nZVNCmTK04/Q7j+hOHymHNsm8v764fHjGEYmFdtgcSkFHFs1oRhcHGyR3pGJuuWkZGJtPQ0pGdkiWNqampo06w+FkwZUbIJBrgkN3L6TAHH30nKvHBfeQlUlNwoU+NKbJqSG6Wmou9NTii5UXoqm9yoqs81lJdsx/W5ouRGGY4vn8mNPNvaMh/LhL+SiEUnpqHVLwcl4g8ePICVlZXMbRFCCCFEuUptTBQo9HdZdEwcPJp3R0JisjhmYW6KDw8vwdzM5FtAit9GDMNg3ba9SEv/PibIQ59u7VHDo/SSBL9LSkxEn1EzcO/xc1bcQF8Pbi5OcHNxgmtFB/HflZwd8xLGGK7nKaX7LZqdnYPqrfvi89cQcczGyhy+D8/DQF+K8aMCnqt3H/0x7891uPmg4JUat676FZNHDvjvePmOuxw+fQmT5y1FeobkGPP3RFIjQwMYGxrAyMgARgb6MDYy+HYzNISRoT6MDQ0QG5+IEdMWITkllVVH5UoVcePkTtjZKPE3MZf/8xTzeOcKBNh44CK+hkWz4mamJpg5cSTMzWSYSFkOcnNzsXr9JggEee+Tdm1a4drth0hISmaVdXG0xYxRfVmrNvKkvM6ChcuErxyvs5Bm8usCDpb92PI6NqiqE9US6XE6/5P3GRwdEwebqk1Zez94eaKKm4ti2lbV822qSqmfhVx+H6lovwlRBeX2NxKRSnn8zlRp9HypDE4LU0n+Fo6OjUfL3uMk4jQOSgghpCBld+VGBdDX05WIHTvniSF9u6FdyyYy12tsZMhKbkxKTimidPnmGxiC8QvXsRIbAeCf3yYqZFa1Ds3qoK7HKrh1GIeklHRxfM7KXXh0fC34Ckr0q+3hiklDuuHoxTvIzMqBhobsbzULUyNc3fsXanu4yrGHZZuNuRFmDmyFLaceICU9SxxPyxJgz01/1PkSj/7NnGGgo1FELfLlZGuBr1Gp8AtPEl/z6ufnB3d3d2hqaGBQYYmNhBCiQDweD3WqV8Hth8/EsX92HpaqjlfvPmHvsQtY9et0jB7YXWHfjYQQQgghhBBCCCGEkNKzcPl6VmIjAGxd/XteYqOUeDwe7Gyt4RvwRRwLi4hWSnKjsZEhbp7egys37yMhKRkuTvZwc3aCpYVZqa0iWVIbdx9hJTYCwKpfZ0qX2FiIGlXdcO34dly/64V5S9fhg18ga/8fa7ZhcK9OClkJcFi/7mhQuzoGT5yH1+8/ieM62lqIjU9EbHyizHXXrVkVV49t//Za/UkvWNVQV8eUIV3xz4ELCI+OF8fjExKxbusejB8xEM5O0q3sKZd+aWjAwc4OX4K+TRyckJiEf7bthqWVNaucvY0FJg/ryUpsJIQQRbGyNEcFGyuER+YlhL9641NgcqNIJEJGRibS09PFk70KhUJUdXeBpiaHJGpCCCGEEEIIIYQQolDl6ur1Gh7umDp2CCuWnZ2DniOm4cGTlzLVmZScgtDwSFbMxLgEKx+VM1nZOVi8YT9qdZuIx68+sPb179wcLf5bkVARzEwM8fvUwazYs7d+OOH5QCHt5eYKcPzyPTAMMLh7awzp0QaWZsao4+GKhjUro7KLPawtTKGtVfyJUwdbS9w/trZcJTZ+V8HCGL8MbgMzI8nBXe/AePx14jW8P8dBVAqDmpEJGdhxwx8RqQxrMY/MzEz4+/tjUMf6lNhICFGaOjW4rUQMfFtVefzc5WjaYyy83/vKoVeEEEIIIYQQQgghhBBlupVvNT89XR3Uq8ltPK6CNXtG9R+TDEqbmpoaenRqg1GDeqN5o3qwsjQvc4mNoeFRWL5hJyvWqG51DO3TRW5t8Hg8dGrTFK9vncTG5QtY++ISEvHXxt1yays/NxcneF05ggE9O4ljHpUrcaqzZZN6uH1mj8xJuKpEV0cL04Z1g5WZMSuenJKKdVv34Oyl68jJzS31frm6VIRQKMRHvwA8efEaYeERyMjIEO+3tjDF1BG9oaOtVep9I4SUX3VrerC2f1+5EQ3b90O1pl1RsXYbWLo3gp59TahbVoGhUx3YeDSHa/0OqNmyJ+q06QPLyk2xbts+5Crhc5UQQgghhBBCCCGEFK9cJTcCwMa/FmHkwF6sWGZmFroNmYQnL95IXd+rtz5gfkiu0tLSRBU3Z469/LncevwKNTqPx/Ith5GTwz5RqK+ngzXzRyu8D1OGdEMlJ1tWbOHa/cjMypZ7W28+BSIyJuG/LR60tTQxqHsrPD+3GU/ObMTH67sR8eQY0n0uIsPnIiKfHsenG7vx9MxGXNu3Asc3LcL25TOxd/UcvL38L9yd7eXeR1VhYaKPeUPboqZrBYl9aVkC7L3lj+XHX+PuuwhkZAvk2rZQxODd1wRsufwRf518g6DoNDg6OkJLK2+gTk9bEzVdbfDO5wPSMzLl2j4hhJRU5zbN5FbXs9c+qN95BKYuWo3EJFqJmhBCCCGEEEIIIYQQVdWycT3WdnpGJtr2GY2wiCiZ68wVsMdiRCKRzHX9zEQiEXYdPoNa7fojLT2DtW/jsgXg8+U/RK+uro7pY4egV6fWrPjmPUcRGBQq9/a+CwmPRBU3Zwzq1RktGtcFny97gmn3Dq3gefRfGBroy7GHZZuhvi5mj+4JO2tzVpxhGNy6/xgr1/+Lr8GKe/4Koq6ugYdPXuBrcBgAQCgUws/PDxkZGXCwtcSssf1goK9bqn0ihJA6+ZIbg0PD8eL1e3z0+4zg0HDExSciMzOr0ONTUtMw7481qN26N+49fq7o7hJCCCGEEEIIIYQQKakruwOljc/nY/c/y5Cdk4Pj5zzF8bT0DHQZPBH/mzEeY4YNgIW5abF1CQQCeL/9yIrV9KgMTc3iV+QrD6LjEjF3xXYcuXC7wP0OtpY4smERHGwtFd4XTU0NrJ43Bn2mLhfHQiNj8c/+81g4aaBc20pLZye5qauroX/nFgUOVGppacJSSxOW+WbkJHkM9LQxvlcTvPwUglN33iA9k52QGpOchTNeQbj4PAT1XM3RrKoV7C30wZdxdt7EtGw884vF409RSEzLYe3T0tKCu7s7/Pz8YGtmADcna6jx+YiJS8T+U54Y2a8z9PVoMI8QUrpaNq6HNYt/waFTl5CTkws9XR3o6epAX09X/Lfuf//q6WhCT1cbejrfti/deIAznndY9TEMg38PnMapS7ew+rfpGNm/m0IutiGEEEIIIYQQQgghhCjO5lW/wffzV7x6+0Ec+xoShnZ9x+De+QOwtrKQqj6hUAjvdx9YMfsKNnLp68/ExzcAk+cvx+MCJtUdNbAn6tfykDxIjtYs/gVXbj9Ebu63RNScnFz8b/k/OLVnvdzbSklNw+lLN8AwDNxcneDm6oTomHj0794Benq6SE5JQ1JyCpJSUpGSkoqklFQkJaciOTUNScnf/s7IzIS6ujomjuiP9UvnQUNDQ+79LOsM9HQxc2QP7Dh+FZ+DI1n7omPjsHbLbrRs2hBtWzSBmamxwvoRn5CIW/e98MDrOTQ0tYAfJnYVCASIj43CoNmjYEBjoYQQJahXq7pc6vnoF4g2vUZiSN9u+HvJPNhYK/56JUIIIYQQQgghhBBSPB7z47KDChYdHY0WLVpIxO+f3wsrCzP5NVSCu5Sbm4tB4+fgnOctiX2amhro260jJo0ejGaN6oFXSJKUzyd/nL54jRWbMmYILGW9LyKOK88JcoovU+ixHFedy82bdVQkEmH3CU/8b81uJKWkSRRVU+Nj9ph++GPGcOjp6oARFD57WvFKnsDGMAzajViIe8/fi2P6ujq4d2Q1alVxLvR5LrRltYJzg4PDo7Hv9A1WbHS/DnCsYCXRH1nxeGoyHwulJqhwazs5l4/jnl545x9SZDktDT7szPXhYK4HBwt92FvowURfCxpqfPD5PDAMg1yhCBnZQoTFpSEkNh0hsWkIjU1HckbR7yMLEwP0bFkTD7yeIzE5lb3PzARjB3aDtrYW+yANPZnuLwBAXav4MkUR5RZfptBjhRwaln1m3m+Hcziex/E1zvV4mdvl+JipKk4/Q7j+hOHymHNsm8v7i8tjJpL8jLt+7wlm/LYWAV8L/mxtXLc6tqxYgNrV3AGhsj5TwPF3EscZ5Lk85pxe49z6XYo/8wtonEPfldlvzp8rXJpW0koHnB9vJa7QoKLvTU64Pl9Ke3+p8mOmrL6r6nMNjt8BSjoWAERcPlOU+RpX4mPG6XOYa9scjuf4fz6ebW2Zj2XCX0nEohPT0OqXgxLxBw8ewMrKSiJOCCGEENVQamOiQJG/y+ITktC2z2i8++jHild1d8Gds/thaVnyBEefT/44fen6DxEet3FRLjidX+V6nrLg36IZGZlYtmEn1m0/CIFA8lxmRYcK8Lp0QPbnX4rfsXOWrMWGHYdYsXvn9qJFvtU8ubb9+Lk3bt5/woq1adawgHYKf43m5uaCx+NBXb2QOZmV+v9bDm3LMOYjFIlw2+strtx7DoFQ8nXG4/FQvao7WjSpj8qVXOQyKaFIJMInv8+47/UcH3wDxOeUhUIh/P39kZaWBj6fB7eK9qhobwM9XR2MHNQbVpbmBdbH43OYW1uNwwTSHK+zYDh9pnD5/2k5HRvkOo5MVAen8z/sz+Ds7BxUa9YVgYWMZ8rCQF8PSxZMw/Rxw9jfQ6p6vk1VKfWzkMt1KSrab0JUQbn9jUSkUh6/M1UaPV8qg8s4agG/haNj49Gy9ziJOI2DEkIIKUi5TW4Evp386jt6BjxvPSi0jEflSpg4ahCGD+gFI0MD1r4T567gk3+geNvJwQ6jBveWrc+Ayic3ikQivPkYiBl/boGX94cCizWqXQXbl89GjcrO4lhpJTcCwJtPgajXe6bExf4VrMzQtkkttG9aB+2a1CrRSoqFJTcyDIOthy4iLjFFHKtRuSL6dGwmUU5W5TW5EVr6YBgGrz5+xdWHbxAVlyR9D/g8iGS4aFVXWxPN67ihU5Pq0NRQR3JKGvafuiKR4NiqcR20alyHfTAlN8pwOCU3lhuU3ChD0/JNbgS+/SZat+MI/tq4B5lZ2RL7+Xw+xg/tjT9nj4ZlCVa3LrhtSm6UHiU3lj4Vfcw4tUvJjdJT0fssj+NlpsqPGSU3St82JTeWKlW92IqSG8UouZEQQgj5OZWV5EYAiImNR+veI/HJ/wsrrq+ni6H9e2DyqMGo4eFebDP7j51FUGi4eNvRvgJGD+krW5+5KmPJjVdvP8K0RSvwNSS8gAOAIb27YN2fc2FlbiJ7u1L8jk1MSoFbk+6IT0wSx+rWqIpn147KlhBXSNuv33/ChWt38orxeJg7eRT0JFb2U9Y5HI5KObnxu8j4FBw6dwvB4dGFlrEwM0XjBrXh7OgAezsb6Ghrl7j+zMwshIZHIjAoBE9eeCMuPrHAckKhEIlx0bC3MYe+ro44rqujXWiCIyU3Sqm8jg1SUk75IcfkRgCIjUvAifOeSE1Lh56uzn833by/9X74W0cbero60NHRxrNXbzF1/jK88flUYEvVqlTCllW/o0WT+tz7TcmN0lPVJEFV7TchqqDc/kYiUimP35kqjZ4vlUHJjYQQQpSoXCc3AkBWVjaGTZ6Ps1duFllOT08Xg/t0w6RRg1GnpgdEIhHWbN6FrB8SAJo2rIsKNpZgGEZ8+9YdBgwDVtxAXw8uFR3YA0gqlNyYmyvA9fvP8PqDP3w/B+FTwBf4fQkrMCECAIwM9LBy/jhMGNRVYtCsNJMbAWDcon+w70zRz3eDGm5Yt3A8mtSpWnjLhSQ3AoCX9wfceOgt3lZXV8OcsX2h88OKfpTcKAMtffGfDMPgc0gUHrzyxRvfIJkSFkvCwcYcLeq4oW4VR2hqsJ/zghIc7WwsMW5wD3YllNwow+GU3FhuUHKjDE3LP7nxu+CwSPyyZD3OXb1X4H4DfV0snDoCs8YOlFyltti2KblRepTcWPpU9DHj1C4lN0pPRe+zPI6XmSo/ZpTcKH3blNxYqlT1YitKbhSj5EZCCCHk51SWkhsBICIqBq16jsDnQlY7aly/FiaNGoz+PToVeN4vLj4RW/YcZsX69eiEalXcZOszV2UkuTEiKgazF/+NU5duFFjUxcke21b9ivYtG//Xdun9jt2y9xhm/LqKFRvWrxv6dmuP1k3rw9BAv5AjS952ZmYW1v17AAJh3rnbDq2aoEn9/L+ZKblRKmoaEApFuO3ljSt3nha4imN+lhZmcLCzhUMFWxgY6EFDXQPq6moQCITIFeQiJTUNoWGRCAmLQExcfLH1qauroVvbxmhWtxqOnb2MkLBI1n59PV1MGzdM4vOCkhulVF7HBikpp/yQc3Ijl7aFQiG27z+O31duQlJySoGHDOvfHasXz4WNVcGr08rSrvTHl8ML/1U1SVBV+02IKii3v5GIVMrjd6ZKo+dLZVByIyGEECUq98mN34ozePryLXYcOIETF64iO7voC/8b1KmBAb26ID4hCRoa6mAYBqcuXIVvwJcij8vPtaIjZk8ehZGDekNXV0dlkhtzcnLRdtB0PH75rkTlB3dvjXW/Toa1RcErPZV2cmNkTAJqdJ+ChKTUIstZmRsj6O5+aGpqFNxyEcmN6RlZWL/3DIQ/DDZ1blkfDWtVFm9TcqMMtAoeaE1OzYDXG388eeOP+OQ0bm0A0NbUQM3KjmhZtzIcbS2KfG9+DgrD4bPXxNvq6mpYNG0kO4mXkhtlOJySG8sNSm6UoWnFJTcCQHxCEpas24mj564jsZDBPUc7a6xYMAmDerQHr6SvXUpulAElN5Y+FX3MOLVLyY3SU9H7LI/jZabKjxklN0rfNiU3lipKbpQeJTcSQgghpBSUteRGAAgNj0TLniMQVMjqggBgamKEUYP7YOKIgajk4iSOP3nxGtfvPhJv6+nq4pcpo6GmxmG8igslJTfm5OTic2AQPgV8xZsPvti85xhSUiXHpTQ01DF/6mgsmjEOOjo/rKhXir9jc3NzUbNNf/h+/iqxT01NDQ3rVEe7Fo3QuW0zNKxTQ+a2z165iXcf/cXb5qYmmDpmcL7zxpTcKBW1vLHpmPgk3Hv6Bs/efEJWNocxvxLS1tJEo9pV0apxLViYGgMAsrNzcOT0RYkEx05tm6NRvVqsGCU3Sqm8jg1SUk75UYaSG7+LiY3H/5atx/5jZwvcr6+niwUzxmH2xBHfruOSU7slP74cXvivqkmCqtpvQlRBuf2NRKRSHr8zVRo9XyqDkhsJIYQoESU35hOfkIT9Jy5gx/7j+Pw1uMiyWlqaqFWtCuxsrXHm0nWZ2zQzNcak0YMxbfQgWFlymAGslJIb9xy/hPHzVxZbzsXBFluXzkCH5vWKLFfayY0A8CkwBOv3nsONR94Ii4ortFzSq1Mw0NctuOUikhsB4JTnA3wIyHsNWZkbY9KQbuLBPEpulEEhyY0/Sk7NQEhUHEIi4xEaGYeQyDgkpxX++tbUUIe9tRkcbMzgYG0OBxszWJoalnhV1YzMLKz5lz1b8ZQRfWBp/kMyLyU3ynA4JTeWG5TcKEPTiktufPcxAJdvPUJObi4EAgGevHyPh89eQyAsuL8Na3tg3e8z0KRe9RK0TcmN0qPkxtKnoo8Zp3YpuVF6Knqf5XG8zFT5MaPkRunbpuTGUkXJjdKj5EZCCCGElIKymNwIAGERUZj92yqc87wFkajo31TtWjbBpFGD0L1ja5y8cA0BX4IAAAKBEPEJidDR0QHDMGDAfPuXwX//MuLzUgzDQF1dHfVqVcOAnp0LXBVSJqWY3Pg1OAzL1m/Hk5dvERgUBoGg6POULRrVxb+rf0MVN+cC2i7d37FXbj1A9+HTiy03alAv7P1nqUxtB4WEY/+J86zYmMF94GBn80OEkhuloiY58W5Wdg5evPPDg+fvEBFd/MqL0qpgZY4WDWuiXg13aGtJJhnm5OTgwPHzCI+MFseqV3VD3+4dWeUouVFK5XVskJJyyo8ymNz4ndfz15jwy2J89Ptc4H47W2v8tWgmhvbrxr52hGO7xR9fDi/8V9UkQVXtNyGqoNz+RiJSKY/fmSqNni+VQcmNhBBClIiSGwvCV4dIJMKdh0+wfd8xXLh6G8JCLuqXJy0tTQzt1x1jBvdGtSqVYGhQfCIXSykkNwqFQni0HQr/LyGFlrEwNcakod3xv0mDoFOCQUplJDeK22YY3H36Dp3H/Q6BgP0cj+jdFvtW/VJ4y8UkN34JicTBc7dYsXEDOsHOxkLctqwouVE6aRlZyMrOgUAgRK5ACDU+HxoaatBQV4ehnnbxJ6OLGRDbsPs4klPyZubt3aklalatlFeAkhtlOJySG8sNSm6UoWn5Jzfm5OTi6l0vvPbxk9iXmZmFj/5fceX2owKO/KZfl9ZYtXAKnB0rFNE2JTdKj5IbS5+KPmac2qXkRump6H2Wx/EyU+XHjJIbpW+bkhtLFSU3So+SGwkhhBBSCspqcuN3oeGR2H34NHYfPo3I6Ngiy9pYWaByJWfUrFYZhgb6OH3hGj76B0rdpoW5KaaOHYrJo4fA4scJImVRSsmNubm5qNm6L3wDJFc/zM/MxBhrFs/GqIE9861a+GPbpfs7lmEYTJy3DLuPnCm2bOAzT1R0tJO6bYZhsGXPUcQnJoljNT3c0btLux9LlbDHBTYg+7FclaHkxu8YhkFgSCRevPVFcHg0wqPji01ULrAJNT5srczhWMEKDWtVQUV7m8Jft/954f0OV27eF2+bm5lg2rhhrDKU3Cil8jo2SEk55UcZTW5kGAbe7z7iyo17ePrqLe4+fIbsnILHUOvW9MC6pfPRonHRk7qXpN2SHV8OL/xX1SRBVe03Iaqg3P5GIlIpj9+ZKo2eL5VByY2EEEKUiMPZZTn6NpWnsnuRhxGCzwPatWiEdi0aITwyGnuOnMaug6dYsxEWh8fjiQcBvv/9fTs3VzLhKDs7B3uPnMHe/waYKthYoaqbCyq7OaOqmwuquLmgqpsLzM1MCm6QzyXhrWQvhXOe9yUSG0f274p6Hs6o4uqIKq4OsLYwLXbwQ364vW4SU9Ix668dEomNLRtUx79Lpxf9n2V+0QMsFR0dYGJshMTkvKQ3709BsKvwLfGDp6wkDYbjc8PlvarG4TUKQNYTdPq6OtDXN5S9WQ2dInfb2NohOSNvYDsyMQM1tQzyCnB5zIQckpYB5V1ky/UzQKknqrh8rnDoN9fvQVU9ucep31wTWZX5fCkpqYYn+X0fE5eAU5duIjY+EfkfU0tzU/Tr1g6W5qZ49e4j5v65DvefeEvUcdrzLi7eeoRpowfgt5ljYWxkIFFGqRf9E+mp7POlosk8nNumRD2Voqr9VuXXCpGOyn4HqDClJYQqMflXmRd6cf6vE71HCCGEEKL67CvY4M8F0/HbnCm4dP0utu8/jlv3vQosGxkdi8joWNx99Aw1qrrLlNgIALFxCViyejNWbdyJEQN7YfakUXCvVMDqhmXI8fPXSpTYOGpgD6z5/ZfCx3CVhMfjYefaxZgyagCu3fPCzXteePjstcTqk9raWjDQl22iTh6Phzo1quDm/Sfi2Ee/QHRu01x+K3USMR6PB1dHW7g62gIAcnMFiIiJR0hEDEIiYhAVm4CcHAFyBQIIhEKoq32b7FVTUwM2lqawt7WEg60VbK3MoKEu3aUitjbsiw7jE5KQk5MDTU0OSYmEEFLKsrNzcOn6Xfj4BgAAGtSpAQ93V9x+8ARvfHwlyr96+wGteo5E7y7tsGrxL6jk7FjaXSaEEEIIIYQQQggpF8rGyo3n9sh/llIuCkl2EAgEuHzjHnbsP4HrdwtfvWjFb7/gfzPHF7qfYRjcefgU67ftw9XbD6XunrmZCaq6uaBV0waYO2U09L8PNnFJlivB6okMw6BB15F49S7vhF7tau54efUgeLlpRRxZTL25JVs1Ut7S0jPRYfRvePaWvUpVHQ9X3D60Cob6ukUez1MvOuENAB4+f4fbXnlJIJoa6pgzbgC0tDSVtwIVl1UfAY7JjYXPNFoimrKt3AgA4HNou5iVHR88eYk7j56Ltx3tbDB6cO+8Aqq6agoXnFdP5HK1qTITK5WYYKiqyY2cKPG55vq+5vIdwOV9/cPswwzDwPv9J1y981giyR8A6lSvjM5tmkJDI+/zkxHl4uKNB5i/bBMCvha8irOZiREW/zIek4b3hYbGDxdHCLJl7zcACDmsREsrN8pwsKomMZXD71yAkhtLnQrPvFxeXyuc2i6HKzeW11UIlblyIyU3ynA8h75zmRwMAM+2pszHMuGvJWK0ciMhhBDycyrrKzeK/XAuPyAwCDsOnsD+Y2eRkJgsh44V0zSPh45tmqFBnZqo6v5tklc3l4rfxtCKUworN4pEItRo1Qcf/SSTOW2sLFClUkVUcXPGkN5d0LheCX8jKnEFcvB4uHn/CboNm4rcXPb56Y3L/4fp44bI3HZ6egbW7zgIoTDvse3StgUa1Kn+3xat3CgVruOpXF8rhcjNzcXKf3ZA9MP/n8cM7QsHO9sfmqaVG6VSLsf2AFpxrBwpYys3RkTF4PTF60hIkvydY2ttCXcXJyxf/y9uP3haYJXq6uqYMmYQFs+ZDFMT4xK3K5XyONGfqq6AqKr9JkQVlNvfSEQq5fE7U6XR86UyaOVGQsqka9eu4ffff8eECRMwfnzhOUpl1YcPH3DixAn4+PggNDQUKSkpAABDQ0NUq1YNnTp1wsCBA5XcS1IWlI2VG1WEuro6enVph15d2sHv81e06jEC0bFxEuWKm6mLx+OhbYvGaNuiMT74BmD9v/tx5PQl5OSU7KL9uPhEPHjyEg+evIR/YBCO7lgr0/2R1q2Hz1mJjQCwYOqIUlylUX6yc3LRb8YKicRGt4oVcGX30mITG0uqVlVX3H36WjzIk5MrgI//V9St7i6X+knZYGNlwdqOjI4DwzAq+d4ghJQP2dk5uHTzPnx8JS/M0dTUQPf2LVC9SiWJfTweDz07tkTn1k2w/dAZLF2/W2IAMD4xGTN/X4ut+05ize8z0L19C/o8JIQQQgghhBBCCCEE+HbxnbwvwOM0iVteQk8lZ3usXTIXyxZMw+lLN7B9/wk8efmmyMMNDfTRv2fH/7rBAw+8b//yeODxvsX8Pn/FnYfPJJtmGFy7/RDXfpgIls/nw8XJHlXdXFDZzRlVKrl8S3ys5Aw9vR/H7hR/UdzlG/clEhu3/70YA3p0gLGRoYy1Ku886TPvd+gzepZEYuOvsycUndhYAnp6unB3cWKt6un9/iPq166m3HPDXNtW2sWy3PrNKcGwiGM11bVhaWmF6Ji86yOi4pLh6CSnFVg5JBhySk78VoPsh3JKJuX6GqOxl/KC22cp19eJ7K9ThkvbnD+C89pmGAbPvd/hxr3HrET87xrVrYV2LRtDXV0dLZo0gOfNe5i35G/4BnxhlRMIBNi08zAOnbyI336ZjKljBheweq3yHm+lokQ/GSip3zRurwT0mJcb9P4qXfRwS48SQmWgoo+ZMiczJoTIVUpKCnbs2IHdu3cDAN6/f6/kHknHy8sLa9euxYcPHwAATZo0QefOnWFvb4/k5GR8+PABV69ehZeXFxYvXoyBAwdi7ty5MDSU9fw7UXWU3Cgjd9eKOLpzLdr2HiWxz7WiQ4nr8ahcCXs2/oW/Fs3Clj1HcPbyTfgHBkEkKtmPi+PnPDF36hjUqVG1xG3K6uKNB6xtLS1NdGzZWOHtKsKs5Ttw8xF7tnw7a3Nc27sclmbGcmvHQF8XbhXt4RuYt7qV94cASm78yeRPbszJzUV8QhLMzUyU1CNCCClcZHQsTl26iYSkFIl9Nlbm6Ne1HcxMjYusQ1NTAzPGDsLwvl2wfOMebNl3UuKiGP8vIeg1ei5aNa6LtX/MQp0qFeV5NwghhBBCCCGEEEIIIQqgo6ON4QN6YPiAHnjr44vtB07iwInzyMrKlihbp0YV7Fq/tNg63/r4YsP2Azh61hMCQeErrIlEIgR8CUbAl2BcuHZHHNfV1cGKX2dhxvjhst0pKTEMg1WbdrNiHu6uGDesL/gqeMHqB7/P6Dp0KtIzMlnxyaMGYun8qXJpo06NqqzkxqiYOERExaCCDc3C/zOxsbJkJTdGRsUosTeEEFK8zMwsXLh2WyJREQC0tbTQq0s7VK6Ul6TN4/HQtX1LdGjVBLsOncaSv7cgLj6RdVxiUgrmLF6Nf/cdw6rf56B313Y00SshhBBCCCGEELkJDQ3FtWvXsHPnTvEqhwCQmpqqxF5JZ8aMGbh+/ToAwMPDA8uWLYOHh4dEuaVLl2LXrl1Yu3YtTpw4gatXr2L//v0FliU/Py7TyJV7rZs1xLKFM1kxLS1NqZIbv7O2ssDyhTPx8fFlpAd74939Czixez3+mDcVA3t1RvUqbtDSyj/j1zdL126Vqf/SMsk3C2l2dg6GTvu9yEHIsigtPRN7Tt9gxcyMDXFt73I4VpD/AFsdD/bKV+FRcYiKTZB7O0R59PV0YaCvx4pFRscqqTeEEFIwhmHwzPs9dh89V2BiY8M61TB2cK9iExt/ZGJsiHV/zMbHe6fQt0ubAsvce/IK9TuPwPTFG1TuNwMhhBBCCCGEEEIIIeVZzWqV8e/fi7F7Q8EJjC4lHBOtWa0y9m9Zia+vbmD+9LEwMjSQqh8ZGZmY/dsqvP/oL9Vxsnr26h2evnrHii2YMQZ8vuoNrQeFhqPjoElISExmxQf16oTNKxbKLRnDxckexvlm1PZ+90kudZOyw8bakrUdGU3JjYSQsis0PBLbDxwvMLHR3tYGk0YNYiU2/khDQwNTxgyG/9OrmDt1DDQ1NSTKfP4agn5jZqJVzxF48Vq1Vs8ghBBCCCGEEKJ8KSkp+PDhA7y8vHDt2jUsXrwY7dq1Q7t27bB27VpWYqMqGT16tDixsUmTJjh79myRyYrjx4/H2bNnAXx7TPr06QMvL69S6SspW1RvBKaMWThrAob07SbenjFhOPT0dDnVqaWliWpVKqF/j074Y95UHNu5Dm/vn0da0Cv4P72KmRNHsMpfvHYX3u8+cmqzJGaOGwhnxwqsmOedx5iyaA0YFVqyXFdHC1r5TjxOGNQZVVylT0otCVenCjDUZ78mvH1KZ/CVlB5rCzPWdnRsvJJ6QgghkjIzs3DiwjVcvfMYQiF7dWhtLU0M7NkBnds0g7q6bIt6uzjZ4dSu1Xhwbhfq15JcTZphGGw9cAYT/remxKtTE0IIIYQQQgghhBBCyoYhfbthwogBEnFXJ+nG1irYWGHV778g9M1t7Nu0AjMmDEf7lk1gZ2td7LEMw2DZum1StSertx/9JGLJKWml0ra8TZy7FBFR7Ak5O7Vuiv2b/pJrsiaPx0Pt6pVZsfef/JGTkyu3Nojy2Vixkxtj4xKQm0vPMSGkbGEYBo+evsK+Y2eRnCK5qkWzhnUxanBvGOeb4L0gxkaGWPPHXHx6fAUDe3UusMzDp6/QsONADJ88H6HhkZz7TwghhBBCCCHk59enTx/Ur18fffr0wejRozFz5kycOHECoaGh8PDwwNKlBU84WNbNmDFDnJjo4eGBffv2leg4Dw8PbNy4Ubw9evRohIaGKqSPpOyi5EaO+Hw+Dv27Bo89j+LZjZNYvXiuwtpSU1ODq7Mjli6YLrGq0p9/K371RjMTY3ge+gdmJkas+O6j57Fi21GFty8vfD4fAzo3Z8VOXLmvsGQLPp+P2vlWb3zn+wW5ubR61c8kJS2dta2mpqaknhBCCFtoeBS2HzwF34CvEvvsbCwxaUR/VClkVlJpNWtQC08u7cOhzUthbyu5GvL+U56YuWSjSk2KQAghhBBCCCGEEEIIAf5Z/j/Url6FFWvdrIFMdenr62HkoF74Z/lCXD+1GyFv7iAp8DmeXjuOvZv+wrxpY9GtQyvYV2AnPZ6+dKNUVm9s36KxxDjPzF9X4dL1ewpvW96eerNXkmpQuxpO7V5X4ApUXNWuXoW1EmRObi58fAPk3g5RHmsrC9ZzLBKJEEMTvhJCypD09AwcOXURtx54SVwDpKeri2H9eqBdyyZSX89R0dEOx3auw+MrR9Gobs0Cyxw5cxk1W/elVRwJIYQQQgghhJSIoaEh7O3t4eHhgY4dO2Lp0qW4desWzp49i4EDByq7e1I7ceKEeMVGAKxkxZLo1KkTOnbsKN4ePXq03PpGVAMlN8oBj8dD4/q1Ub929VJpz0BfD3OnjGHFLl2/i1dvFb96o5uzIy7sWwdtLS1W/Pd1e3Ho3E2Fty8v4wd2Ym1/CY3CnSdvFdaekx178DUrOwfRcYkKa4+UroTEZImVGp0d7ZTUG0II+YZhGDx65o19x88XOCtp0/q1MHpQTxgbGci1XT6fj6F9OsP3wWn89b8p0NLSZO3feuAMFq3ZIdc2CSGEEEIIIYQQQgghiqWtrYVLR7aha/uWcHa0x8rfZqNBnRpyq9/QQB8N6tTAqEG9sXrxHFw8vA1v752XWFVp6VrFr97o7GSP7X8vZsVEIhEGT5yPF699FN6+PNWtwU5INdDXg56erkLaMjTQR6WKjqyY9zvFj1+T0qOpqQk9PR1WLCk5RUm9IYQQtq/Bofh33zF8/hossc/JvgImjRoIV2fHAo4sucb1a+Gx51Ec27kOTg4VJPYnJaeg8+BJ8PlEyf2EEEIIIYQQQgp39uxZvHjxQpzMuGnTJgwcOBD29vbK7prM1q5dK/67Y8eOMt2XiRMniv8ODQ3Frl275NI3ohoouVFFTR0zWGL1xqXr/i3RsSKRCKcu3sCuw6eRmm+1uZJoUq8GDm9eypqVEQDGLvgbtx97S12fMjSs6Y5qbuyTlrtPXlNIWwzD4I4X+3Hh83kwMdJXSHuk9H0K+MLa1tPVkZhNmBBCSlN6egaOnLmCWw+eSsxKqqujjaF9OqN9y0YKXWVWR0cbC6ePxumdq6Guzm5n9bbDWLHloMLaJoQQQgghhBBCCCGEyJ+ttSUuHdmGzy+uYcGMcQpvz9jIELMmjmDFzly+gXcf/BTe9tihffDbLxNZsYzMTHQfPg1fgsMU3r68jBval7V9++EzBAaFKqy9ujWrsrbDIqMRn5CksPZI6YqNi0daWgYrlj8BmRBCSptIJMK9R89w8MR5pKWzr4Hi8Xho1bQBRgzsBQN9+Vyjw+PxMLBXZ3x8dBmrF8+BoQG73oTEZLTvPw4BXySTLAkhhBBCCCGEkJ/R33//jZSUvEnQBg0aJFM9Hh4erKTInTt3cu4bUR2U3Kii9AtavfHGfbx886HI4xiGwYhpizBwwlxMnLsUrfuMgUAgkLr9Pl1aY/0fs1kxgUCIvpOX4L3vl0KOKjt4PB7GD2Cv3nj+1hPExCfJva2X7/wQGhnLijWtWw16ujqFHEFUzSd/9mu+smtF8Pn08UoIUY6gkHBsP3gKn7+GSOxztLPBpBH9UInjrKTS6NquGQ5vXibxufjb3zuxad+pUusHIYQQQgghhBBCCCFE9cycMFwpqzcCwJ/zp2LkwB6sWExcAroMmawyCXt9urSFqYkRK7b7yFmFtZeVnS0RS0lNU1h7pHR98vvM2jY00IetjZWSekMIIUBqahoOnjiPe4+fgWEY1j4DfT2MGNALrZo2VMj1G9raWpg3bSwCnl1DuxaNWfuiY+PRrt9YhIRFyr1dQgghhBBCCCGkrDl58iRru0mTJjLX1bFjR/HfKSkpuHZNMQuYkbKHsm9U2NQxg2FuZsKKFbd648Zdh3H0rKd42/vdJ9x9/EKm9meOG4RZ4wezYilp6egyZiHC8iXzlUVDe7SGtpameDs3V4CD527JtY2U1HTcevyKFTM1NkCLBjXl2g5RnpTUNIRFRrNiVdycldQbQkh5FxuXgEOnL0uszMzj8dCycT2MHNBdYvbQ0jCgR3vsWr1AIj5ryUbsPXG51PtDCCGEEEIIIYQQQghRDUaGBpg9aSQrdvbKLbz18S322MSkZAybNB9VmnTD8vXbJZIeisPj8bBj7RK0a9GIFfcPDEbPkTOQmZklVX3KoK2theH9urNi+4+fR25urtzbSk/PwPW7XqyYoYE+7CtYy70tohyf/NnJjVXcXcHj8ZTUG0JIeff5SzD+3XcMQSGSKyq7VnTEpJGDUNHRTuH9sDA3xbkDm9GsYR1WPDQ8Cu36jUVUTJzC+0AIIYQQQgghhCiLl5cXa9VGDw8PTvVVr16dtX3ixAlO9RHVQcmNKqyg1Rsv33yAX1duwoVrd/ElOAwikUi8z+vFG8xfukGiHs/bD2Xuw9rfZ6JvlzasWHhUHLqOWYjklLI9C6eJkT76dWrGiu0+ea1EA5sMw+Dp64/Yc+IKjl28jYfP3yEoLAo5OeyBQM97z5CdL9a9bRNoaKhzvwOkTPAN+Mra1tbSREWHCkrqDSGkvHvvGwChUMiK6evpYkT/7mjdrIFSV5UdPaArNi6ZJRGf8L81OHn5dul3iBBCCCGEEEIIIYQQohJmjB8GE+P8qzcWPeGrUChE/7GzcfTsFfh9/orFqzbj2p1HUretqamB03s3oEZVN1bc68UbjJi2SOJ8bFk0fmgf1nZ0bDwu3bgv93Zu3PdCRmYmK9a1XQuoq9O46M8gMSkZkVExrFgVN1cl9YYQUt7df/wch09dkPje4fP5aNeyCYb26w49Pd1S64+eni4uHfkXdWtWZcU/fw1Bh/7jVWbFZ0IIIYQQQgghRFrHjx9nbVerVo1TffmTI/MnT5KfVxkZSWD+u5URPyQElipG+nanjuqPtVv3Ii4hURxbuXG3+G99PV1Ur+yKalVc4Xn7EQQCgUQdV289xIbfpsjUZT6Ag//8jsioaHh5fxDH3/t9Rb8pS3BlzwpoamrIVHfJcJsJcsLgrjh84Y54OyAoAg9efECrRkWvrPglOALX7j8Xb/t9CQUA8Pk8WJmbwM7aAjraWvANDGEdV6uqKyra2/zXdQ591zCQ/ViuBNmyH8t15k4BhxlwdRRz4vpTQBB+fB26uThDTV2TXYiRfN+VmAyfC/JDM61Kj8t3GcfHW8oZp+WK03ubw7GqPBswj0uSYeHPdXBYJOtxcbSzwYAeHfMG7zi1C4Dh8nzxMX3MAKRmZOK3NTvEYZFIhGEzl0JXRxfd2jXl1j/CwmXGbIbrHChK/f5SUcr8HCekrOPync/1vcXlu1NVPws5/15Q0fvNBdfHTGk4Pleq+tXF9TXK5bxhQZ9J9BuAEEIIIaRY31ZvHIXFqzaJY+c8b+HN+0+oVb1Kgcf8sWYL7jx8xoqdOOeJzm2kPwdpaKCPK0e3oXGXoQiLiBbHz1y5hbl/rsOGpfOlrrM0VXV3QdMGtfH4+WtxbNfh0+jTtV2JjheJRIiJS4CWpgaMjQwLPO8YGBSCtx/82O26ucDdtSJU9z8P5Ee++VZt1NXVgYM9TfhKCCl90TFxuPvoqUTcyNAA/Xp0gn0FG6WcozQyNMC14zvRqtcofPDL+8z08Q1A58ETcev0Hhga6Jd6vwghhBBCCCGEEEV68uQJa9ve3p5TfQUdf/XqVQwcOJBTvaTsU9Wrr8h/9PR0MW/qqEL3p6Vn4Mmrd9h1+CzCI2MKLOP/JRifg8Jk7oOOthYu7FwKt4p2rPhtr9eYsGh9iVZCVJamdT1QxcWBFdt98mqxx/n4BxUYF4kYRMYk4MU7Pzx4/o61T09XGx2a15O5r6TsycjIRFBoOCtWxc1ZSb0hhJR3AoFA4ru+aYPapToraUksmjYS/5s6ghUTCIToP/lX3Hn8Ukm9IoQQQgghhBBCCCGElGUFrd7YrPtwNO48GBPm/IHNu4/gvtcLJCQm4crN+1ixYadEHVfvPIJIxskqKthY4cqRbRJJCRt3HsY/Ow/JVGdpGj+sL2v7xr0nCAoJL6R0HoFAgD1HzmL7gRPYuOsw1m7bh+PnPPHomTeCQyOQm5uL3NxcXM63EqS2lhY6t20u1/tAlOuTHzu5sXIlF/D5dLkJIaT0BQaFSMQqV3LGpFGDvyU2KpGZqTFunNoF14rs65BevvmA7sOmIiMjs5AjCSGEEEIIIYQQ1RMaGiqxqiLX5EYAMDRkjwV8+PChkJLkZ0Jnm38CU0YNRLXKrpzq8Lzjxel4MxMjeO5dAUszY1b84LmbWLxhP6e6FYnH42HcwM6s2OmrjxCfWPjStQzD4EtolNRtdWrRALo62lIfR8quz19DWMm7GhrqEiepCSGktERExUIgFIq3eTweHJQ8gFeYv+ZPxLRR/Vix7Owc9By7AE9evVdSrwghhBBCCCGEEEIIUQaG241R1k3E8SaU6maor4NfJo1kPXIZGZl49uoddh86jZmLVqB1r1Ewd2+KXiOmF/hIx8Yl4OUb2S+CqF7VDWf3bYCGhjorPuePtThz+abM9ZaGft3aw8jQQLzNMAz2HDtX7HE+vp8RHpW3WmV6RiZ8P3/FrQdPsO/4OazctAtb9hxFYjJ7bLV9y0Yw0NcF59coX132G0+N243Pl/3G48l+E+Zwu/H4cr+lpmUgNCKK1c8qld0ky5biZwLrxhmPw01Z7XJtm5QmHo/H6aa6rxMO/S7iczI4LIK1Xa2qGwb26QYdXR1un7/fb5zuMg821pa4eXoP7CtYs3Y9fPoKfUbPRHZOrmLa5kpZj5kyPwuVdp85Umq/uT5f5fHGgTw+0+gmxY3j72alv9boRrdibgr4v2Lp3HhKvCnrM4EQQlSHl5dkDlL+xERZGBkZsbZ9fHw410nKPkpu/Ano6enC68ph7N+4FNPHDkarJvVgamJUaHlbawt0a8eeKfPq3SeFlC45ZwdbXNq9XCKB76+tR7D7hCfn+hVlRO920NTQEG/n5Obi0PlbhZZPTE5Fcmo6K2ZpZgw+v/Afla5OFVDNvSL3zpIyJTMri7VtYWYKjR9eS4QQUpqCwyJY21YWZtDW1lJSb4rG4/Hwz5JZGNW/CyuenpGJLiPn4LWPn5J6RgghhBBCCCGEEEIIKaumjxsCM1PjYssJhYUnO3nefsipD22aNcSeDUtZMYZhMGzqQjx+/ppT3Yqkq6uDYf26sWL7jp2HQCAo8rjPXyVXx/qRSMQgOTWNFXO0s0GdGlVl6ygpk/wCAlkTvmppaqKiI/cZ2AkhRFoMwyAk35iom0tF8OSStCU/jva2uHl6D6wszFjxG/e8MGTivGK/fwkhhBBCCCGEEFVQ0IqK8li5MX8dtHJj+UDJjT8JfT1djOjfDRuXzcOd0zsR63MHYd7X4XlkM1b/NhPD+nZB/Voe6N6+Be6e3oXh/dmDV/eevkZ6RibnftSuWglDe7aViM9dsR2paRmc61cEMxND9O3UlBXbdfwqRCJRgeXzr9qor6eDycN64n+ThmBUv05o17Qu3J3toaf7LcnT3sYCvdo3K3MnUwl3JsbsJOKEpGTWwB4hhJSm/MmNDvlmAy1r+Hw+dq1ZiP5d27DiySlp6DhsNj4FBCmnY4QQQgghhBBCCCGEkDLJ0EAfJ3auhbOjXYmP0dXRYW1fvcUtuREAhvXrhuUL2atDZmfnoOfIGfD7/JVz/YoyYXhf1nZEVEyRyZ4Mw+BrSLhUbaip8dGtfUsaF/3JhEVEsrYruVSEurp6IaUJIURxYuMSkJmVzYo52ldQUm+K5ubihBundsPEmL1ixTnPWxgz47dCr0kihBBCCCGEEEJURWhoqEQs/6qLsjAwMChRW+TnQmecf1I8Hg+21hawtbZAp9ZNJfZbmJlATU1NPHNpdnYO7np5o1s7ybIlkZmVjX2nr2HtrlMICouS2J+SloGE5FQY6OvKVL+ijR/YBccu3RNvfwoMwcDpf+HguvnQybfq1ZcQ9uBNRTtr8Hg8aGpqwMnOGk5235JJGIaBSCSCmpqaorsvM5FIhKj4FIRGxSM+OR25AgFyc4UQMQzU1dWgoaYGXR1NVLA0gYOVKXR1yuYKYMpiaW7K2s7KykZqWjoMDfSV1CNCSHklEokQGh7Nijna2SqpNyWnpqaGQxv/QEZmFq7cyVuePi4hCe2HzMCD0//C2bFsDkgSQgghhBBCCCGEEEJKX5vmDRHwzBNRMXF4/ykA7z764/1Hf7z/FICP/oHIyckVl63p4Y4/5k5Gn9GzxLEXb3wQHRMHK0tzTv1YOGMcgsMisevQaXEsITEZXYdOxYcH56GlpcmpfkWoXsUNDetUxzPv9+LYrkNn0KNj6wLLx8QlfJsc94dExU6tmyI+MRmhEVGIjo2XmPSzVZP6sMg3fkZUX/5ERjU1mkObEKIcwaHspHtjIwMYGUpe8FhWVK/qhmsndqJd37FITUsXxw+fvgQ9PV1sW/M7TQhACCGEEEIIIURllWbCYWhoqFxWhSRlFyU3llPGRgZoWr8mHjz1Fsc873pJndwoEomwce9JrNpyELEJSYWW69W+KRxsLWXtrsK1bFgDbhXt4P81TBw7c/0RwqPjcX7HEliaGQP4b4bSfMmbzg4FJ4/weLwyl9iYmpGFj4ERCImKR0hUPMKiE5CTKyzx8ebGBnCwMoaDlQlc7MxR0dasXJ9oNTI0gKamBmugPDAoBLWqVSnXjwshpPRFx8YjOyeHFXOws1FSb6SjqamBk9v/QrdRc3DXK+93SUR0HNoPmYkHZ/5FBWsLJfaQEEIIIYQQQgghhBBSlvB4PNhYWcDGygIdWjURx3NzcxHwJQTvPwVATY2Pru1aQE1NDQb6eqyEgmt3H2PkwJ6c+vDk5VtERMZIxL8Eh8Hz9kP07tKWU/2KMn5YP1Zy49U7j/DomTeaNawjUfZLcBhr28TIEI3q1RRv5+TkIjwyGmGR0UhOSYOdrRVqergrrvNywjAMBEIRcnMFEDEiaKqrQ11dHXw+je0VxsLcjLUdExuvpJ4QQsq74DB2cqNDhbI/2Wv92tVx6cg2dB40EZmZWeL4jgMnoK+nizV/zKHrSwghhBBCCCGEqKTSTG5MSUkptbaIclByYznWuU0zdnLjHS8wDFPik2YikQjjF6zCvhOXCy3j7myP+RMHYniv9mX6ZByPx8OWJVPRZexvEAjykv2evvmExv1m4sruZajs4oCo2ARkZrGTR5zty3byCMMw+BoehwfefnjtGwyBUCRzXXFJqYhLSoW337cvIhtzQ7So5Yr6VR2ho6Uhry6rDB6PBwszU4RH5q2WduHqHdz3egkPd1d4VHaFjZUFyu4rnxDyswgOjWBtmxgZwkBfT0m9kZ6OthbO716NjsNm4an3B3H8a2gE2g+ZgXsnt8LSWHXuDyGEEEIIIYQQQgghpPRpaGigqrsLqrq7sOLtWzbG2Su3xNtXbz2UKbmRYRhcvfUQq7fswcMfxljzq2Bddid8HdizI2YvXiNO9hSJRGg/YAIObPoLA3p2ZJXNn9zo7GjH2tbU1EBFRztUzBcvS9LSMxESGYOQiBiERsQiNDIWCckpyLfgJIBv56ntrM1hb2MBB1tLONiYwcLUmJIeAVjmS26Mi0+Q6roCQgiRB4ZhEBIWyYo52ldQUm+k06JxPZzdtxE9hk9Fbq5AHF+3bR8M9HWxeO4UJfaOEEIIIYQQQggpW4yNjSVipZlISZSDkhvLsa7tmmHhik3i7ZDwaHz0/woPd+dij2UYBlN+XVtoYmODmpWxYOIg9GzfBHw+X259VqR2Tevg2r4V6DtlKZJT82Zv/RoahaYDZuPstj+grs5eidHU2ABGhvql3dUSyc7JxcuPQXjg7Yew6ESFtBEZl4ITt7xx/v47NPBwRItarrC1MFJIW2WVtaU5K7kRAJKSU/D4uTceP/eGqYkRmtWvidrVaTVHQojiJKemsbaTUlLhG/AFlSsV/51eVhjo6+HK/nVoO3g63nwIEMd9Pwej0/DZuHd0AwwNKMGREEIIIYQQQgghhBAinc5tm7OSG6/f84JAIIC6esmHyhOTktFn9Czc93pZaBkdHW2sWDQDDepU59RfRdLT08WE4f2w7t8D4lh2dg4GTZyHryHhmD9tNHg8HoRCIYLD2JPq5U9uLKuiYhPw8IUP3vl9QUJSaomPy8zKRkBQOAKC8lYF09bUgKujLZrUrYpqlRyhpqYa497yln/lxlyBAJev3UINjypwsK9AY6CEkFKRlJyClHxjoo72ZX/lxu86tmmGYzvWYuD4ORAK8yZdX7JmKwz09TB70kgl9o4QQgghhBBCCJFOYSspGhoaKqS9pKQkhdRLyg5KbizHPNxdYG9rjdCIKHHM8+6TYpMbGYbBzD82YOeR8xL7Ojavh/kTB6FVo5oqOYjRpnEtPD65AV3H/Y7g8LyEtcTkNHQYtRBj+nWElbmJOO5cBk+UMgyDVx+DcOrmC6RlZpfoGGM9TVQw04OOpho01Png83jIFYqQKxAhPjULEfEZEIgKmML0P9m5Ajx8E4iHbwJRt7I9+retDQNdbXndpTKtfq1qeP/JHzk5uQXuT0hMxsXr95CRmYVmDeuUcu8IIeVFRYcKePLyrXibYRicvnQTw/p1g5ODasxYCgAmxoa4dmgDWg2YCt/PweL4mw8B+HvXCSz7ZYwSe0cIIYQQQgghhBBCCFFFnds2Y20np6TC68UbtGhcr0THJ6ekotPASXjxxqfA/SbGhpg2ZjCmjRkMC3NTzv1VtKXzp+KT/xd43n7Iii/86x8EBodi68pFiIyOkxj7ciqD46LfCYVCvPP7igfP38P/a1jxB5RQVk4ufAKC4RMQDBNDfTSrVxVN6lSFob6u3NpQBXp6utDV0UFGZqY49urNe7x68x4G+nqoWtkN1aq4w87GQiWvESCEqIawH65tAgA+nwdtLS0l9UY2fbq1x96NyzFy2kJWfM7iNdDX08X4oX2U1DNCCCGEEEIIIaRsS00t+UR2RDVRcmM5xuPx0LltU+w8dEYcu3r3CaaP6oeI6DiERcUgLDIWYZExCI2MQXhkjPjv6NgEVl3q6mo4tWUxerZvWtp3Q+6qVnLEk9P/oMeEP/Dyvb84npsrwI5jV9CqYQ20aPBtxlVnBxtldbNAyWmZOHH9Gd76F77srjqfh8r2xnCy1Ie9hT4cLPRgoKNZZL0CoQiRiRkIiUlHSFwa/MKSEZeSVWDZV76h8AuJwaB2dVHbXTVmcOXC2soCU8cMgfe7j/DxDUB8QlKB5e4+fgHXig6wtjQv3Q4SQsoFNxcnNGtQG4+evxbHBEIhjp27ilGDesLGykKJvZOOpbkpbhzZiBb9JiMoNFIcf/cpUIm9IoQQQgghhBBCCCGEqCpba0vUqlYZb3x8xTHPWw9LlNyYmpaOLoMnF5jYWMHGEr9MGonxw/pCX091kt10dLRx/sBGzPh1FbYfOMnat/vwGYSGR2La2CGsuLWlOfTK4H3MyRXgjtdrPHjxHsmp6QptKzElDZfuPIfnvZeoVdUZXVrWh7WFSfEH/gR4PB4qOtrjg6+/xL7UtHQ8e/kaz16+RlU3Z/Tu1hEaGhpK6CUh5GeXP3laJGJw5PRFjBzUB9raqpPkOHxAD6SlZ2DqgmWs+KS5f8LB1godW6v+dVeEEEIIIYQQxRAKhYiLi1N2N1RCeno6MjIyAACmpqZQU1OText6enrQ19eXe72qIjk5WdldID8ZSm4s57q0acZKbrz3xBu6bq2lqkNNTQ3HN/32UyQ2fmdtYYq7R/7GsF9W4cKtJ6x99569g0AoRNum9eBkZ62kHrJ9X63x5I3nSM/KKbCMmYEWmlW1RuPKltDXkW5ASV2ND3tzfdib66MprCBiGPiFJePhhyi8D04Ak29Rx7SMbOy+6IU67vYY0O7nX8XRyNAArZs1RKumDRAdE4cPfp/h4/sZiUl5X9pCoRDnPG9j/LC+UFenj15CiPy1bdEIGZlZ8H7/SRzLzsnB4dOXMWZwb5iZGiuvc1Kys7HErLEDMWvJP+JYanqG8jpECCGEEEIIIYQQQghRaZ3bNmclN169/RCrfp8tUU4oFCI6Nh6h4VEIi4zGpl2H8eTlW1YZGysL/LVwBob06QJNTdVM4lJXV8fWVb/Cxcke8/5cx9p3/a4XfHw/o3/3DjA0+HZxjrNj2ZvQ9EtIJA6dv4WY+KQiy2moq8OugjUcKtjCwc4WFWytoa2tBQ11dfD5fOTmCpCbm4vY+ASEhEWIbympaQXWJxSJ8MrnM95++opurRugbZOa4PP5CriHZUv71s2RnpGBoJDCV8b86PcZRoYG6Ni2ZSn2jBBSXlSu5AwrCzNEx8aLY5HRsTh25hKGDeipUonVk0cPQlp6BhYszfsOZhgGOw+dpuRGQgghhBBCSKHi4uLQokULZXeD/GfatGmYPn26srvxUzIwMFB2F4gSUIZNOdemWX1oamogJydXpuP5fD4ObVyMPh1/vpNrerraOL31d8xduQsb958Tx7U1NZCUko5nbz5BV0f5SXtZObk4dNkLb/xCCtxfxd4YrarZoIq9Mfh8XoFlpMXn8VDF3hhV7I2RmJaNx5+i8cAnChnZAlY5b79Q+IfGYFTXhqjiVDYSQRWJx+PB2soC1lYWaNO8EW7df4LHz73F+6Nj43H/yUu0bd5Iib0khPyseDweunVoicysbHwK+CKOp2dk4uDJixg7tA8My+DM2oUxMmDPaJOSpthZtwkhhBBCCCGEEEIIIT+vru1bYOXGXeLt958C8NeGnYhPTEJ4ZDTCIqIRFhmNiKhYCIXCQuuxtjTHnTO74e5asTS6rVA8Hg9zJo+Eo50NRkz/FVlZ2eJ94ZEx2H3kLIb06QJrK4syldyYkyvA5TtPcefJa4kJWL/T1/s/e3cd3caZ/Q38O7LQIDNbxpjjOIlDDjNzGmpSZt7tdvddare/7nap3W677bZbhkDD1DCjAw4ndszMbJktmPcPN3LGbI/kkez7OSfHmauBK1nkeebexxrjx8Zg1PAoeLq79qgzu6eHG4ZFhhmWq6rVuJechnMX45GVm99ufa1Ohz3HL+LmvQw8snQaPFyd+nyfLIGDgz0eX7cK5RWVSLiXjIR7KSgubT9TwKWrNxEaHAR/X/N5zhBCBgaxWIz1K5fgm807UFmlNsSz8wqwY99hrF62wKKKzX/98pPIyS/Ef7/ebIiVlFUImBEhhBBCCCGEENJzarW6+5WMqKqqql+PR/rfwCxuZPU8t+9kFMTU+B6X7XygrTO21jJMjY3B0TOXer2tjbUCX/zjt1izeDbQ3HHnSpPrw33mbM52PZAlEonwwR+eQ6DKA79893NYiRh4uzvjbkoW7qZk4eCpi5g/bWzfDs7wKDQUKwAA9Q1N+HTLSWTml7RbxVYuxupJgRgR5NL34/SAo60MC0f7YnKkB7aczcDtLO7J1tr6Jny28zweXzIJI0M8+R1M19T9Op3i8fqykvV6EwbAtCkTkZadj+KiQkP8/OUbCA3yh49XD4s9+b6fMTym0ebzHOVNwGPzeS9mLGewhIP375rH9oI+z/gQ6LsC0OXzTGQlwopFs7Fpx35k5rRebFFdU4cN2/fjidWLYW2t6PuxRXy+OvbuMVPaKznLNXUNgFUfO67qtd2v0yV+3zf6jOX7nsLzM4QPXu+HPPLm/dIU8rXN4/1QqL9fhCbYY8bztcnweY7z/F3zesyE/J7D4zHj+11DqO+GvP8O4Lc5Pxb6nZjHn068f196S33MBHyO83ltivj8snkSS9vHrDqIEUIIIYQQoxs7MgpOjvaoqKw2xN78+8e92oersxOOb/9yQBQ2PuihRbPh4+WOJY++itLySkO8tq4el67dxqRxMfD15jnGZyQZuYXYsLvz2RoD/VWYPH4MRgyLhETM71IIB3slYseMROyYkcjOzcfZi/G4ev02NFru+eas/GL87X/bB80sjs5Ojpg8YRwmTxiH0rJyJNxLwflLV6DVtpxHZ1kWew4cxQtProNM1vsxVkII6YqdnS0eWbUU32zagdq6ekM8OS0T+w6dwJL5M8FY0Phz7KhoTnFjZ7MGE0IIIYQQQggh5kapVHa/Uh/V1NS0izk4OJjseMQ8DMziRtIr/++lx3Aq7io0Gu5AjK2NNVSebvDxdIO3pyt8PN3g49Gy7OPphuAAHyjkws9c2B9efnQJ3F0c8ewfP0J6bpEh/uiv/obrP30OX2/3fs+ppq4B/9l4AAUl7Tu3jQxyxsqJgbBT9LEIpA+U1lI8MycU19LKsO18JmcWR51ej2/2nEXj3NEYPyyw33ISmlgsxrIFs/HldxsNXX5ZlsXugyfx/GMrIZGY9vej0+lQ39iIhsZG1Dc0oqGhqeVnYyNEIhG8PFzh5e4GqbT/nieEENMTi8VYs2w+vt+6BwVFpYZ4aXklNu8+iEdXLraI171dm1km1bX1naxJCCGEEEIIIYQQQogls5wL8LmEzLv3zS6srKwwZ+p4/Lj7UJ+O6ORoj2Pbv0BEaFCftgfAs2kR3+ZWXf++xo2MwsUDG7Bg3ctITs8CAPh6eSAzJx/5hSV45zcvQSox3cUqnXqgcdDlm/ewcc8x6PXtHwt/lTfWrFgEXx8vk6Thp/LGIypvLF84G4dPnMXJsxfBPvD7vD+LY3puEZ5aNR8SKx6/L97NYPg0iepdMxhXVzdMdXWDVCbD0RNncP95VlVdg6OnLmDRvJk93herF6jJICE9xK9gzlI/64XU+WPm5OiI9SuX4rsfd6KxqbVB9s2796BQyDF72iQwfBrP8alR7+V7sFJpx1lW19bxaDbLt6G+hTbSFKyYlV7XFsWCip4HBnq8Bw16bZGe4vU9w0KfZ7zSFvA+G3tyD0udLIQQQoggqLiRYNqEUbh7fDOu3UmCo72doYDRXmkrdGpmZeX8ySgoLsfrf/3cEKuoqsHqV/6MM1v+3a+FIvWNTfhk08F2hY3WMjHWTjb9bI2dYRgGo4JdEeJtj81n0nE3u7W7K8uy2HzoCqRiK4yK8BMkPyF4uLtiSmwMTp6/YoiVV1bh+NlLmDdjUo/2wbIsmps1aGhsLU5s+dmy3Pr/hgfWaUJTU3O3fxwwDAN3Fyf4eLnDx7Pln7OTg0V1MySEtCeTSbFuxUJ8u2U3yh7oYJ1XUIytew9j7bJ5EPPsWm1qSjvu9xB1TZ1AmRBCCCGEEEIIIYQQQgaChxbP6XFxo0Qibhk38XJHcKA/fvPyEwgJ8rfcC/97INDPBxd++h7Ln/wlktKykF9camje+cRrf8Tu7z4SbPzo9KWb2H7wTLu4WCzGwjnTMGPyeFhZmX6Wdhtra6xYNBfDoyKwYetulJSWc26/k5yJTzfuxXNr5kIuGzyztI8bHYPk1HRkZ+cYYtdu3kFYSBCCgwbWTKeEEPPg4e6Khx9ajB+27IZW19p4+2L8DVgrFJgUO0rA7HpOadt2PJRmbiSEEEIIIYQQQsjgZN5XtZN+ExygQnCASug0zN6rjy/Fuat3sfvoBUPs8s17+H//+BL/fvPFfsmhWaPFZzsPI6+YO1hmby3Fywsj4Olk3cmW/UdpLcWzc8Ow/XwmziW0znTJAvj+wCXIZRIMDTJN51RzNHHsSKRkZCOvoNgQu3z9DjzcXGCtkLcpUmxEff0DxYoNDahvaDQMHhsby7IoKi1HUWk5rt5KBAAo5DJ4e7jBx8vDUPQol8tMcnxCiOnY2FjjkZWL8fWmnZzCwPSsXJw4dxlzpk0QMLvuKe1sOMsNjU3QaLSQSOjrKyGEEEIIIYQQQgghpPeWzpuOX7/8BLbsPgSZVGooXvT++aePpztU3h7w8XSHi7MjRKLB113eydEeBzZ+gukPPYOSstYmq/uOnMa/Pvseb7z4eL/ndDLuBnYePtsu7q/yxiNrlsPT3bXfcwry98XvX38RPx0+0W4Wx5TMPHyy4Se88uhiyPqxOa6QRCIRli6Yi8+++hbNzRpDfN/BY3jx6UehUMh5H6OlGWxzyxhqfUNrM9iGRtT/PJ56///3x1kbG5tgY23982vcAz5eHnB3c+mXQlhCiOn5+nhh1dL52LJ7P/T61hkTT5yNg7VCjpjhQwXMrmc6avbKsiw1oyaEEEIIIYT0ymzkQQHTXGduSRpghaPwaRffuXMnXF2Nfw7Rxsam+5UGMHt7+349np2dXb8ej/Q/ujqckF5gGAZf//113E7KQHpOoSH+0bc7MXHUUKyYN9nkOew8dRsZucWcmLOdDK8sioSLkv/AkLGIGAarJgZALrHCsZv5hrhez+LbfXH4w1Pz4KQcHB/qIpEIS+dOx+cbtkOjae0auPfwqe43ZvXdr2NkDY1NSMvKRVpWriHm6tw6u2NUeHC/zlRKCOk7pZ0tRkSF40zcVU78dmKK+Rc32rb/jKipq4eTg1KAbAghhBBCCCGEEEIIIZaOYRj8483X8Y83Xxc6FbNmY2ONXd98gBEz16CsotIQ/927H2FczDBMHDuy33KJu57QYWHjjCnjsXT+LEGL1KQSCVYsmouo8FD877vNaGxsMtyWmVeML7YcwvMPL4BEPDgK6RwdHTB7+mTsP3zCEKuprcPBoyexYsl8zrp6vR6NhkavLQWJdbU1nRYu3l/uSzPYxqYmlFdW4nZiEgBAIpbAy9DkteWfXZviIkKI5QgZEoCl82dh1/4jnPj+o6egkMsQERYsUGY907a4UafToaGhEdbWCoEyIoQQQgghhFgiBXSwZqi4EWzHYVdXV7i7u/dvLoOYWq2GUmn863wdHByMvk9iXqi4kZBesrezwdb//AETVv0STQ90nnzq/72P6PAgDPH3Ntmxk7KLcf5WBiemtJbg5YXmVdh4H8MwWDzWF81aHc7cbZ3BsbFZi82H4vHSqimDpuOci7MjZk4eh0MnzgtyfLlMCoVcDmuFHAq5DPUNjSgqLYNe38k3uTZKyytQWl6BG3fu4eyla1i3fD7cXJ1NnDUhhI/m5mYcOHYWtxKS293mbgGv37YzNwIt3UqpuJEQQgghhBBCCCGEEEJMy9vTHZs+/Svmrn3RMCuhTqfDmud+gxvHt8HVxcnkOSSkZmPz3hPt4ovnzcSc6ZPMZowxZEgAfvnCk/j4ix9QW1dniCdl5GHj3pN4fPlMs8nV1GKGRyEpJQ1pGdmG2J3EZNTW1UOj1aC+vhENjY1obGzizHYJACzbPxfgabQaZOflIzuvtTmvvdKOU+zo6e4GsZgupSHEUgyLDEN9fQMOn2wthmdZFgePn0HIkACzfj23LW4EAHVNLRU3EkIIIYQQQggxe6YoYLyvqqqqX49HzIP5nsEhxIyNiByCj/70Mp7/w78NMXVtHVa9/A4u7PgPFHKZ0Y/Z2KzBpsPXODGpWISXFkTA1d78ChvvYxgGKyYEoKZBg+vp5Yb4vawixN3OwIToIAGz619jRkQhKTUTmTn53a/cCZFIBIVcZihStFYoHvj/Az8VP99mbQ2FXA6RSNRuXxqNBgVFpcgrLDb8q6mt7zaHanUNftxzCM+sW0En1QkxU6VlFdi29zBKyyvb3ebl4Yal86YLkFXv2FgrwDAM5wIHdW1dF1sQQgghhBBCCCGEEEIIMZZZU2Lx5uvP4p1/fW6IFRSVYP1Lv8PBzZ+adNbEuoZGbNx3ul0B3LIFszFr2kSTHbevVN6e+OULT+CDT79BXX3rWNvVO6mICvHHqCjznjnMWBiGweJ5s/Dp1xu4M1lm5wqYVfeq1TWoVtcgISkVAGBlZQVfby/MnjYRnh5uAmdHCOlOXV09UjOy2sVr6+qh1+v7P6FesLPtoNlrbR083F0FyIYQQgghhBBiqZif/w129Bj0P6VSCbVa3S/Hsre375fjEOFQcSMhffTMmgU4H38HG/ccN8RuJqbhtXf+iy/++rrRj7f79B1UqLmFZ0vG+cHbuf3JTnMjYhismRyEjKIaVNU1G+K7Tt5AeIAHnJTmfx+MgWEYrFg4Czv3H0N2XiGsrESwVigMBYktxYlyTnGiQip+4DYZZDJp77q7Mp0PLEskEvipvOCn8gLQ0r1QXVPbUuhYUIy8ohIUFJVCp2vfKbWySo0d+49h/UMLOyycJIT0v+bmZmTnFSIjKxdXbyVAo9G2W2fMiCjMnhpr1h1K72MYBko7G1Sraw2xnIJihAb6QiaTCpgZIYQQQgghhBBCCCGEDA5v/vJZxMXfwvGzlwyxY2cu4i///gJ/euMFkx13++ELULdpyDlnxmSzLGy8z9PDDS8/8wg++t93aGxqLezbdvAcQgK8obS1FjC7/qNU2mHB7OnYue+QyY4hEj04xqqA4uex1Af/r5DLIZNJUVZeibyCIuQVFKG8sn1DyI7odDpk5uTim0078OS6h6jAkRAzlp2bjx37DqGmgwapY0YOg1Rq3mOKMpkUMpkUTU2t19Goa2q72IIQQgghhBBCCDEfKpUKCQkJnFh1dTXvWRZramo6PBYZ2Mz/ynZCzBTDMPjsL7/A9YRUJKZmG+JfbTmASaOj8MiyWUY7VlJ2Mc7fyuDEgr2UmBTpYbRjmJq1TIy1U4Lw2cF7hlhjsxabD8XjpVVTelewZ8Fsbazx2OolYFm2Z/dZ3744yVQYhoG90g72SjtEhg4BGAZarRbFpeXIKyzGtVuJKCmrMKyfkZ2HI6cuYN6MSf2WIyGklU6nQ15BMTKyc5GZk4+8giLo9WyH68pkUiyeMw2RIQH9nCU/SltucePiJ/8fAEAhl8FBaQsHpd3PP23h4mSPVQtnYP702EHzmUIIIYQQQgghhBBCCCGmZGVlhY3//RtGzlqNgqISQ/ydf/0P40cPx6wpsUY/5u3kTMTfTuHEIsOCsXjuDKMfy9j8VN5Yv2oJvtqwzRCra2jElv1n8MzquYPm3PXQiFCkpGXgTmJyt+tKpRIo5HIo5NKWJrDW8g4LF1tuU8BaIYdU2vNmsAF+KoweOQwAUF/fgPzCYuQVFBoKHpuamzvdVqPV4MddP+HZR9fAtoPZ1QghwtHpdLgYfwMnz11sNzsjwzCYPmkcJo4bJVB2vaO0s0VpU+t1GB0VahJCCCGEEEIIIebIx8enXXGjMWZyrK6ubheLjIzkvV9i3qi4kRAebKwV2PbJWxiz9CXUNzQa4i/88UOMjAxGZIg/72OwLItdp25zYlKxCOumDoHIwgbAIn0dMS7UDZeSWwc/72UVITGzCJGBngJm1v8sZfBSLBbD29Md3p7uCA8OxJcbd3JOpl++fgfurs4YOSxCwCwJGRxYlkVRSRkysnKQmZOP7LyCDmdnbMvDzQUrF8+Bs5MDoG8/E6s5U9p1fLFAQ2MTGhqbUFhSzolv2HUET65eiP/99Q2LmJ2SEEIIIYQQQgghhBBCzJ2bqzN+/N8/MH3F09DpWs4xsyyL9S/+FtePb4O3p7vRjlXX0Igf95/lxBRyOdatXGIxY2sjo4dixK0E3LjdelHPraRMXLubhlFRwQJm1n8YhsGyRXMR4KdCeWUV5DJZB0WKLYWL98/lszqNyfOytlYgOMgfwUH+LcdkWZSVVxgKHfMKilBSVg6WbW0kqa6pxdY9B/DYmuU07kCIgFiWRXFJGTJzcpGRlYvs3Hw0a9q/b9jZ2uChxfPgp/ISIMu+sbO1QekDTaY/+WoTzl26Bgd7OzgolS0/7ZVwUNrB2cnBqJ+7hBBCCCGEEEIIH1FRUThy5Agn1lFhYm+1LZCkWRsHBzr7SghPEcH++N+7v8Cjr//dEKtvaMSql/8Pl3d/ClsbBa/9p+eXI7+U+ya/ZJwfXJRyXvsVyvLx/kjKq0JVXWsXzLPXUwddcaMlUtrZYvWSOfhu6z5ota0FVQeOn4OLkyN8feh3SIgxsSyL8ooqZObkISM7D1k5+WhobOx+wwfEREdg3oxJFjvgHh0RjITkjO5XfMA3W/ejvLIamz9+Gwq5zESZEUIIIYQQQgghhBBCyOAxaVwM3v3dK/jtXz40xErLK7H2+d/gxI6vIJFIjHKc3UcvQl1bz4mtXDIPDvZKo+y/v6xZtgCp6ZmorWu9L9sOnkXEEBWsFZY5xttbIpEII4dHCZ1GlxiGgauLM1xdnDFiWEvn98bGJvy48ydk5+Ub1svNL8TBY6exaO4MiymyJcTSsSyLisoqZGbnITMnF5nZeahvaOhym0A/X6xYNAc2NtYA2C7XNSdKW1vO8u6Dx7H74PFO1x8XE43P//V/iIoIMXVqhBBCCCGEEEJIlzqaTTE3N5f3ftvuIyKCJmEaDERCJ0DIQLB+6Sw8s3YBJ3YvLQfP/+HfnM6OfXHuZjpn2VUpx6RID177FJK1TIy5MT6cWEJ6AcqrawXKiPSGj5cHFs2ewonpdDps3XsY1eoagbIiZOC5GH8T//7f9/jk6004cOwM7qWk96iwUSRi4OvjiSnjR+G5x1Zh0ZxpFlvYCAD//ONrmDV5bK+LFPcePYe5j7yOqmp6XyKEEEIIIYQQQgghhBBjeOPFx9uNEZ2/fAN/+NvHRtl/dU0dLt9O4cSGhodg7KjhRtl/f7Kzs8Wa5Qs5sbqGJsTdSBIoI9JTcrkMq5bOb1dQe/12Aq5cuyVQVoQMDjU1tbidkIQ9B47hw/99i4+//AH7j55EQlJql4WNDMNg2sRxWL9qyc+FjZbFzcWpV+tfunYLU5Y8ivOXrpkoI0IIIYQQQgghpGfGjx/fLpaTk8Nrn21nbQRaZogkA5/lXu3eFZ7FZPyOre/7tnpt9+t0RafhsW1z9+t0gWV1fd9Yz2Nbvvjc7zZdGT/64/O4eisZNxLTDLHN+07g0WUzMHtSTPvtRd2//NR1jbiRnMeJTYz0gMjCO0KODnbFnkvZaGxu+d2zAM7fTMeSKdFdbyiS9v2gTN9ruRmRVd+PC4C14pE3n/cUE4mODEVRSRkuXm0dwKurb8CWPYfx5NqlRurOK2QnRYFeX4K+ri31PUXAvHm8p7Rs3/lNGVm5OHI6rovjcF8fHm6uCPTzQYCvD3x9vCCTdfGew/B9T+Fxv5nev5d6eXrgyJbPAABN9bWorqlFZXUNqtQ1qKqubfmprkVBcRne+2wjGpuaDNueu3ILU1a9jEMbPoSXq0Pf8+ZLyJcXj+/EDHg+V3h8r+X1VZ7veymvY/N8X+Dzmc/nfrMC9rvh+57E58ki5Ocun8ecd9pCPc/4frfj8X2c77F5f3b2lYCvTb5/g/B6nvJ5fQj4PsyXiM+xebw+9Bb8POPDit/f+PyO3cF3dSvjzHBDCCGEEEKIqYlEInz70V8QM2s1svMKDPH3P/0OE8eOwOI503jtP+5GEvT61r8VJBIJHn5oscXOlDcyeiiGXb+N2wmtBY3nryZg+rhoiESWeZ8GCxsba6xZthBfb9wOjbb1Gowjp87B1cUJgf6+AmZHyMCh1+uRmp6F9KwcZGTloKyistf78PZ0x8wpExDgpzJBhv3j0TVLcfT0hV5tU1WtxuxVT2PLF//C4rnTTZQZIYQQQgghxFIwjMCXIJsJBhD28vNBKjIyEgkJCYZlvjM3drT93Llzee2TWIaBWdxIiADkMim2ffwHxCx5CeraekN834mLHRc39kDc7Uzo9K2fshIrEcaFuvLOVWgyiRXGhbjh9N1CQyzuVgbmTxgKiVjAiwxJj82aEovS8kqkZbZ2VygsLsXew6ewYuEsix1oJsQcFJeWdXm7s6MDAvx8EOingr/KG9bWin7KTFgymRRuMqdOu5fOnDgai598A9Xq1pmA7ySlY+LyZ3H4+/cREkgXGxBCCCGEEEIIIYQQgXV0lQnvhjBCXa3B87hCNaulx5sXJwc7bPvyn5i05Ak0N7cWfT3+6h9x59QOeHu6t9+oB81odHo9zl9L4MRGj4hqN3uepZk5ZTynuLG0ohrJGbkID+qiCIdn01VezX/4Du/1ocmhYVOGT0Nkfs9vtoPXh4e7K5YtmIVtew8aYnq9Htv3HsIzj66Gk6MDr2NaOksdC+7od90f2/J/uCzz8Wa6eD/S6/XYsHUPsnJam30zPbifTo72CPBTIcDXBwF+qk5nauTz++L3ePd+24dXLIKvtxdOXbiMyqpqVFXXoFpdg6pqNaqqa1ClVqOySo1qdQ3nfjU2NmHFE6/hiw/ewRMPrwDv7wq87jafJmqCdorlsamADTwJMXuW+vy2zM9bQVnqe5mFfpe1aJb6mAv2nZInXucFjNwc19j7I4SYpdWrV+Ott94yLCcmJvLa3927dznLKpUKKpXlNjUiPUfFjYQYUZCfF3719EP404c/GGK37mX0aV96lsX525mcWMwQF9jIB0ZH/4mR7pzixtqGJtxKycOoCD8BsyI9JRKJsGLBTHy1aRfKK6sM8btJaXB3dcakcX0r6CWEAOI2M8lIJGJEhA75eZDOB/Z2tgJlZt4mjR2OMzs+w7z1v0RhSWuBaFZuISatfAkHv3sfMVGhAmZICCGEEEIIIYQQQgghlm/08KH419u/wiu//7shVlVdg8837MQ7v3mxT/u8m5KNKnUdJzZ5/BheeZqDoAA/eHm4oaCoxBA7G3+36+JGYjYiwoIxpXQMzsRdMcQaGhvx486f8PQjqyCTyQTMjhDLVlRcyils7IytjTUC/FQI9FchwE9l8UXvnZk4LgYTu7nGor6+AWue+SX2Hz1tiOl0Ojz12h9QUlaB37zylMUWHhNCCCGEEEIIsVxtixtzc3OhVquhVPbtb/gLFy602z8ZHKgknhAjixkazFm+nZQJvb73HbtKK2tRqa7nxCZFdtDt1EJ5OFojxNueE7uXVSRQNqQvFAo51i6bB5lMyomfPH8FyWmZnWxFCOmORMItYndxcsSyBbMwPCoc9ko7gbKyDMPCg3F+9xcIDuBeGFJaXoVpa17B8fNXBcqMEEIIIYQQQgghhBBCBo4XH1+N5fNncGJXrt/p8/7OxnO7cfv7+sDXx6vP+zMXDMO0K9K8k5KNiuoagTIivTV14jiEhwzhxErLK7Br/1GeM8MRMrg1azQdxuVyGcKCgzBv5hS89PR6/Orlp7Fi8VyMGBY5YAsbe8raWoFd33+Cx9cub3fb7/78L/zqrX/06dokQgghhBBCCCGDm1qtxtatW/Hee+8hLi6uT/toW4DY1/0AwMWLF7vcNxm4qLiRECMbHh7EWa6pq0dmbu+L9nKKKjnLSmsJ/NwGVlFLlJ8jZzm3zX0m5s/F2REPLZzF6QDIsix2HTyB0rIKATMjxHJJpdziRo1WK1AmlinA1wvndn2OmKgwTry2rgELnvg1tu0/KVBmhBBCCCGEEEIIIYQQMjAwDIPVS+dwYrcSU/q0L3VtPZIyuLN3DYRZG+8bMzKa0yiUZVlcu5smYEakNxiGwbIFs+Du6sKJJ6dl4NS5SwJlRYjlk7Zp9sowDJ55dA1+8+pzWLNiEcaOGg5XF2eaibANsViMrz96F7955Zl2t334v+/x2Eu/RXNzswCZEUIIIYQQQgixRLm5uZgxYwbeeustfPXVV3jiiSfw6quv9no/zzzD/Tt169atfcrn8OHDUKvVhuWnn366zzNAEstDxY2EGJmnmxNcnbgzEt68l97r/eQUcwv9VC62vPIyRypX7n0qLKtGs4aKeCxNcKAfZk4ex4k1NTXjxz2H0NDQKFBWhFguiVjMWW5u7rhzKemcm4sTTm77L2ZMHMWJazRarH3lbXzy/U6BMiOEEEIIIYQQQgghhJCBYXhkKGe5uLQcRSVlvd5PdkEJZ1kmlSImOpJXbuZELpchJnooJ5aVX9LJ2sQcSaVSrFm+ENYKBSd+9uIV3L3Xt6JeQgY7iYQ7HsqyLDzcXSES0WVs3WEYBn9/61f4159/2+62TTt+wpL1L6Gurl6AzAghhBBCCCFCEdE/w7+B4sECv/uqq6uNfpzXXnut3bGOHDnS65kXVSoVZ4bFuLg45Obm9jqfL774wvB/pVKJX//6173eB7FcA+k1TIhZYBim3eyNN+9l9Ho/uW2KG31dbXjlZY5ULjZ4sM+enmWRX1IlVDqEh/Gjh2NYRAgnVlFZje0/HYVerxcoK0IsU9uZG5s1VNzYF3a2Ntj/3QdYtXAGJ86yLF7904d464OvwLKsQNkRQgghhBBCCCGEEEKIZQvyV8HGmlvsdfNucq/3k1NQyllW+XhC0mZGL0sX6O/LWc4tLO1kTWKuHB3ssXLJvHaFV3sPHkdhERWrEtJbbcdDgZYmpaTnfvn84/jh039C3KZx7pFT5zFzxRMor6jsZEtizjQaLbLyinD2yh3sOnIeW/efxoY9x/H9rqPYtPcEth08g5+OX8S1uykoraii8W5CCCGEEEIGqI4KA/tSLNidhISEDuMXLlzo9b7eeOMNqFQqw/Jrr73Wq+0PHz7Myeejjz7qdQ7Esom7X4UQ0lvDwgNx7MJ1w/LtXhY36lkWucVVnJiv68CbuVEmsYK7owJFlQ2GWE5xJQK8XQTMivQFwzBYNHsKyiqqUPDAAF5Gdh6Ono7D3OkTBcyOEMvSduZGDRU39plMJsWmT96Bi5M9Pv1hF+e2v/znexSXVuLTv7wOKysrgTIkhBBCCCGEEEIIIYQQyyQSiRAdGYK4+FuG2M2EZMydPqFX+2lb6Ofr42WU/MxJ2/tUXlWD2vpG2FrLBcqI9EWAnwrzZkzBgWOnDDGNVoMtu/fjmUdWw9Z24DUrJsRUpBJpu1hzczPkcpkA2Viu9SsXw8XJEQ89+Srq61uvu7l87TYmLVyPw9u+HJCfqwNJdU0dbidlIqegGDkFJSgoqehVA3GFXAZfLzf4erkh0NcTkcF+NPZNCCGEEEKIBVOr1Th06BC+/PLLDm974okn8Mwzz2D8+PFGOZ5SqexwlkhfX98O1u5+X99++y2WL18OtVqNhIQEvPfeez2afTEhIQFvvvmmYfmjjz4y2n0kloOKGwkxgfYzN6b3avuyqlo0NnO70qkGYHEjAKhcbDnFjblFFQJmQ/iQSCRYs3QuvtiwA7V19Yb4pWu34e7qjBFR4QJmR4jlaNupVKfTQ6fT0SBEH1lZWeHj//sF3F0c8acPvubc9uWP+1BeWY2NH75Jg6WEEEIIIYQQQgghhBDSS8MiuMWNtxL6MHNjYRlneSAWYXi6u0IsFkOrbR3/zS0sRXiQqoutiDkaNSIKRSWluHbrriFWra7Btr0H8ejqZe1mUCOEdEwiaf9a0Whp5sa+mDtjEk7s+g4L1j6LispqQzwpNQMTF6zDoa1fIDIsWMAMSVssyyItKx/n4u/gRmJ6r4oZ22pobEJyRi6SM1pmcFHaWmPCqKGYEBMJR3s7Y6VMCCGEEEIIMbLDhw/j/fffR3V1y99xHRUYdiQuLg5xcXGcmFKphL29PQDg+PHjvcrjjTfewFtvvdVuf/PmzevVfu5TqVTYtWsXnnjiCeTm5uKrr75CTU0N3nnnnU63iYuLw2uvvWZ4DD766CPMnTu3T8cnlk0kdAKEDETDwwM5y7mFpSiv7NmHDgCo65o4y3KJFRxs2neuGwg8HBWc5Zr6pk7WJJZAaWeLNUvntivC2n/sLHLziwTKqndYlkVVtRqFxaVoamoWOh0yCEml7d/vNRoazOODYRi8+erj+OzdNyAScb/+7jp8BnMe/RUqqnr+OU0IIYQQQgghhBBCCCEEGB4Zylm+lZDSq+2ra+pQXVPHiQ3E4kYrKyv4eLpzYm1nrCSWgWEYzJ81FX4+3px4Tl4BDh47DZZlBcqMEMtiZWXVbsyuuVkjUDaWb2xMNM7t3wSVtycnnldQhMmLHkHclRsCZUYe1NDYhLNXbuPd/27Ch9/uwrW7qbwKGzuirq3HodNX8Na/v8OXWw4gKT2XPpsIIYQQQggxU7m5uVCr1T0ubOyMWq1Gbm4ucnNze73t6tWr8c4770ClUkGpVGL8+PHYtWsXlEpln/NRqVQ4fvw4nn76aQDA1q1bMXr0aLz33nuIi4tDbm4uEhISsHXrVjzxxBN44oknoFarDYWRVNg4eFHbOEJMIDRQBZlUgqYHTr7eSsrA9NjhPdpeq9VxlqWSgVuHLBVz71vb+04sj4+XBxbNnoI9h04aYjqdDlv3HsYz61fAXmk+3eFYlkW1ugaFxWUoKCpBQXEpCovLUN/QMpuoRCxGTHQkxo8eDqXdwJw9lZgfSQddfZs1GppZ0AieW7cErs4OePjV/+MMkJ67cgsTV7yIg9+9B3+VZxd7IIQQQgghhBBCCCGEEHLf8KHc4sbk9CzU1TfAxlrRyRZcxeVVnGWZVAo3F2djpWdWVD5eyMrNNywXlVUKmA3hw8rKCquWzscXP2xBtbrGEL9+OwEebq4YExMtYHaEWAaGYSCRiDnNhjVaKm7kIzwkCOcPbMK81c8gMTndEK+sqsash57E1q8+wMLZ0wTMcPBiWRZX76Rg+8EzqKtv7HZ9hmHg5uIMH28PWCsUkEjEEIlE0Gp10Gg0qKyqRk5eAWrr6jvdh17P4mZiOm4mpiPI1wvrl82Em7ODEe8VIYQQQgghhI+5c+fi+PHjhhkXH9STwsKOCiLvzwLZW6tXr8bq1av7tG1Xfv3rX+O5557DoUOHsHXrVnz11Vf46quv2q03fvx4rF69mooaCRU3EmIKYrEVhob449rdVEPsZmJ6j4sbNW0K/CRWA7e4UdzmvjVTceOAMHxoGIpLy3Hx6i1DrLauHlv3HsETa5ZAIpF0uJ1Go0F2XiEycvJRUFQChmGgkMtgrVD8/FMOhULe8lN+/6cMcrmsXWfHtjouZCxFfUPnJ481Wi0uXbuF+Jt3MTIqHBPGjICDfd+7URDSE1YdvOdrNDSYZyzL507Boe/ex9Jnf4ea2tYBn6T0bIxf/jwOfPseRgwNETBDQgghhBBCCCGEEEIIsQxDQ4dAJBIZZh1iWRZ3k9IwdmRUj7ZvajNLl42NdbfjPZbKztaGs9ys0QqUCTEGGxtrrF2+CF9v3M4pyDp88ixcnB0R6O8rYHadY1kWNTW1LWOlRSUoLi2DXCZDWHAgwkKCwDCM0CmSQUQqkXKKG2nmRv5U3p44+9NGLFr3Ai7G3zTEGxoasfSRl/Hff76F5x4z/gWrpHPVNXXYuv8Ubt3L6HQdiUSCqIhQBPh6Q+XjBZW3JxRyeZf7ZVnWUOSYk1eA5LRMZGZ3PEtLek4B/vrfTVg8czymjosesN+1CCGEEEIGM4Zp+TfYMQBgQROXq1SqPm/bUQEkn9kWTUWpVHKKJ+/PMGlvbw+lUsnrMSADj3kUN7Jsyz9zoedRXMXy2Vbf920BnnnzfPz55i4UHveb7Wbb6PBATnHjraQMzjaMvovHzJxeD/2sZcCkkxNpMh4fuoxV37fl+/zm877Al4DfVmdNiUVJWQXSs1pPoBYUlWDv4VNYsXAWGIaBXq9HQVEJMrLzkJGdj9yCIuh0vX+8GIaBXNam+PHnnyIRg6KS8m4LGbui0+kQf/Murt1ORHREKCaNi4GTY/tuGfweb56/K4bPCWi+nwF8tudxv3ndZ5jlAK1Op8PuAyfAtLlvepYBI2r52tbd50+XeL+f8bjYgufvCyIeX1vbHHva5Fic3fUFFjzySxQUlxriRaUVmLL6FWz//G+YMzW278czFiG/X+l5XljD43OXYXgcm+dnLsvn+zTf93Fe2/N5HxbwecbyfF/g9ZALeL/5fP7w/juBz3diHsfm/ZHL4/fF9/OeFer7At/fNZ/HjMfzBOD5POXz2uT5mPH6m5Hn+xmfz3w+2/L8VfM6tl7Ax0zP9+8fI5/7s9RzaoQQQgghhACwtlYgJNAPSWmZhtjNu8k9Lm5s1/BVYh6XL5hC2/um0VDDV0vn4e6KpQtmYvveQ4aYXq/H9r2H8Myjq+Hk6NDl9vcLUyqrqiGTSmFtrYBCLodcLjPKGBbLslDX1KLw50LGwuKWn3X17Wf6upVwD24uzpgUOxqRYcFU+EL6Rfv3RSr6NgYnRwcc2/ENVj31Cxw8ftYQ1+v1eOGNt5GTV4i//P41sxwrH0gMszUeOIO6Tq5TcXN1xuTY0Rg3agSsezjr9X0Mw8DJ0QFOjg4YHhUBAMgrKMK5i/G4cu0WmpqbOetrtDrsPHwONxLT8MiyWTSLIyGEEEIIIUQQKpWKChpJpwbu6AAhAhseHsRZvnUvvcfbSsTcKwybtQP3QjdNm/smseJ7dSUxFyKRCA8tnIWvNu1CeWWVIX43KQ1WVlZobGpCVm4BpxtjX7Esi4bGRjQ0NgJVpjsJr9frcePuPdxMSEJUeDAmjY2Bq4uTyY5HBhedToed+w4jKZXbtdHWxhqODh0U0xJeoiNCELfvayx49BdISG59zGvr6rHwsdfx+T9+hyfXLBYwQ0IIIYQQQgghhBBCCDF/w4eGcoobbyUm93jbtg0vxQN4nLDtfdP2odknMT+RYSEoKS3HmbgrhlhDYyO27NqPp9avhEwm46xfW1uHjOxcZGbnIiM7F9Xqmnb7FIlEbZq6thQ9WlvLW34qFFDIZYZiyPvr1dU39KiQsTMlZeXY+dNhnD5/GZNiRyEqIhRWA/g1SYRVUVmF+oYGTqxZQzM3Gou1tQK7f/gEz/7yLXy/dQ/ntr99+DnyCgrx5b//DKlUKkyCA1xzswY/7D6GGwlpHd4+NDwE0yfFIjQ40KhFpj5eHli7YhGWLpiFK9du4fiZOJRXVHLWycgpxF//uwmrF05D7MgIox2bEEIIIYQQQgjhi4obCTGR6PBAznJiWg6ampohk3V/crBtcWOTRgeWZQdk57TmNh1ZxWLqAjmQKBRyrF02D19u2skpYryV0POB7f6gkMvg6e4KL3dXeLq7wcvDFQzD4Pzl67hxN6nd4DrLsridmII791IROyoas6dOEChzMlDo9Xrs+ukIEpO5AxxSqQSrly2ARCIRKLOBzdfbA+d2fYnlT/8Gpy9eM8R1Oh2efuMvyMkvwp9ef2ZAfv4SQgghhBBCCCGEEAGwLM/ZzgcQPufc+DyGfM/18fn18Tk076eN6Z530ZGh2LLnsGG53RhQF78vqzazw2m1A7fgr+0slWKxVcfPR3HvZm5qhxGyGM0y39/4jgFMmxSLktJyTvPK0rIK7DlwDEsXzEZOXgEysnKQkZ2LktJywzpsJ4+XXq9HfUNDu8Kv/lJeWYk9B4/h9IXLmDh2FIZHhUMsNualRcKNufD76BHw+c3rOcrz8TbBGFllVTW+37IbjU0agGn9HJArFIDo5/cwlmcDcF6/Lz7fM/he72K8LxoSqQzffPJ3qHy88Jd/fcq5bcO2fSgoKsWO7z6GvdLu58355D4Ix1I7ebwaGpvw2YZ9SM/Ob3ebrY0N1q5YiBHDIk2amkIux5QJYzFu1AjsPXQMp89f5tyu0eqwcc9x1NY3YNbEmF7uXagvwzzR32CkJwbhWxl/FvqgWep7Al271HuW+rsmhBBCBikqbiTERIaFBnCWtVodEtNyMCJySLfb2tvIOcvNWj0qa5vhZCfrZAvLVVjJHZSxt+U5YEfMjouzIx5aOAubdx3s0aCPSCSCt6cbAn19oFDIUN/QiIaGppafjY0//2xCfUMDNBptr/ORy6Tw8nD7uZDRFV4ernCwVz4wcNl6ImDh7KmYHDsKcfE3ce1WAjRa7vFYlkVc/E1EhYfA092117kQArQMUu/efxQJSamcuEQixrqVS6Dy8RIos8HBwd4OhzZ+hCdffwc/7j3Kue2df3+FnIJifP7330EisdyvzSzLorauHsWlFSguq0B1TR0CVJ4IG+JPhZuEEEIIIYQQQgghhBBehkeGcpZvJ6ZCp9P1aMY3aZuCqbbjMAOJts19kxi1WIwIiWEYLF80B19t2MYpXkxKzcA/Pvpc2KK4TshlMni6u8HdzRkZWbkoKStvt05VtRr7j57E2YtXMGncaIwaEUVjCoS36mo1vt+8o92spd5eHhgS6C9MUgMYwzB453evQeXtiRd//TanqfOJsxcxeeHDOLDlS/h4eQiY5cBRW9+AT77didzCkna3xQwfitXLFsDWxqbf8pHJpFi1dAGGR0Vg47Y9KCvnzuK45+gFNDY1Y9GM2H7LiRBCCCGEEEII6QydMSfERJR2NghUeSIjt9AQu5WU0aPiRmd7ayhkEjQ0aQyx3LLaAVncmFNay1lWeTgJlAkxpeBAP8ycPA7Hzlzs8HY3FycE+vkg0M8Hfj5ePZrhFGgZCDYUO9Y3tPxsbGotgmxoRLNGAycH+04KGbuntLPF3OkTMXHsSFy6egtXbtxBs0bDWaftgDQhPaXX67H34DHcSeR2sr5f2Oin8hYos8FFJpNiw8fvQOXtgX9++gPntu+2/oTColJs+/xvsLPtv8Gm7rAsi8oqNYrLWgoWS8oqW/7/cwFjyf2f5ZUoLq1AQ2NTu324uThi6riRmBI7ElNjR1KxIyGEEEIIIYQQQgghpNeGD+UWN9bVNyA9Kw8hQX7dbiuTSjjLNbV10Ov1EIn4znxlftQ13DHRtvedWDapVIq1Kxbhyx+2oL6+0RDvSWGjXCaDVquDVmea8cb7hYxeHm7w9Gj56ehgbxgPYFkWSSnpOHsxHoXF7Qty1DW1OHDsFOrq6zF14jiT5EgGB7W6Bt9t3oGqajUn7uHuivWrlvWoKJ70zTOProK3pztWPfUa6utbG5DfSUzB+LmrcXDrlxgaESZghpavsakZn36/q11ho7VCgXUrF5t8tsauhAQF4A+vv4TdB47ibNwVzm2Hz8RDJpFg9uRRAmVHCCGEEEIIIYS0oOJGQkwoOjyQW9x4L6NH2zEMA5W7A1JySg2xnNI6RAc4Gz1HITU0aVFa3ciJ+bpTceNANX70cOh0Oly+fgcSiRj+Km8E+vkgwNe7zwVDYrEYSjtbKO1sW4MmKsyxtbHGzCmxUChknCJNuUwGLw83kxyTDGwsy+Knwydw624SJy4WW2HtikXw9/URKLPBSSQS4e+/fxkqL3e8+ub7nAsOjpy5hKDxyzA8MgThwQGIDAlEZGggIkMC4WBvZ9K8UjJysGn3EeTkFXKKFUvKK/s0e+2DSsoqsW3/CWzbfwJAm2LHsdEIG+JHxY6EEEIIIYQQQgghhJAuubs6w93VGcUPzFh3MyGpR8WNHq6OnGWNRoOikrIBOe6Sk1fAWW5734nlc3Swx8ol87Fh6x7o9fpO15PJpPD39UaArwqB/iq4OLeMj2s0mgcaurY2cq1v+Hm5vvHneAMaGppQ39CAxiZuY8PuChk7wjAMwkOHICwkCGkZ2TgTdxl5BUXt1kvNyKLiRtJnNTW1+G7zDlRWVXPi7m4ueHTNCigUcoEyGzzmz5qC03s3YOHDz3Fmmc0rKMKkBQ9j1w+fYtoky3iN19c3oLi0DMUlZSgpK//5/y0/S0rLUFxahtq6eoQHB2HKxLGYOmEsggJMN+6p0Wrx+ca9yM4v5sSVdrZ45dnH4O3pbpLj9oZMJsWa5Qvh4uSIXfuPcG7bezwOCoUMk0ZHCZQdIYQQQggxBrrKjxBi6ai4kRATGh4eiN1HLxiWb91L7/G2vm7c4sbcNjMcDgS5ZXWcZSuRCF6uDsIkQ0yOYRhMjh2FybGW3fHtXgq3SDk8OJC6SJI+uXX3Hm7cTuTE7hc2Bvr7CpQVeenxlfD2cMXDL73JuSigrKIKx89dwfFz3G6WXu6uiAgJQERIoKHoMSI4wChFj2mZuRi3+ClUVdfw3ldPdFzsOAJTxo3A1HEjqNiREEIIIYQQQgghhBDSoeGRoThyOs6wfCshBasWz+l2OzsbBRyVtqhUt46D5uQVDLjiRo1Wi/xCbsGDr6erQNkQUwrwU2H+rCk4cPS0oYmilZUIKm9PBPr7IsDXB95eHhCJRO1mdZRKpZBKpbBX9nx8Qa/Xo6GhpehRLBbDXmnX5/P4DMMgOMgfQwL9kJmdi217DnLGSVycqEkx6ZuWwsbtqKis4sTdXJ3x6JoVsLZWCJPYIDRqRBTiDm3F/NVPIyU9yxCvVtdg7son8X+/fQ3jx4xAZFgwnJ36twi/trYOBUUlPxco/lywWFqG4tJylJS0/L+krBzFJeWoravrfocArt9KwKYd+wAA3p7umDphrEmKHX86dgEpmbmcmIO9Er984Um4upjXe+fMqRMgl8uw+efH5b5tB07Dz9sdvl4D6zsYIYQQQgghhBDLQcWNhJhQdHgQZ/nmvQywLNujE2S+HtwThTmltT3e1lLklnELNr1c7SERU5EYMV9V1WrktRl8jggN6mRtQrrW9kIGAIgdPQJBAd13syamtXTuVJzc9inmPPwKaurqu1y3oLgUBcWl7YoevT3cfi56DHig6DEQ9krbTvbExbIsXn3rX0YvbLRWyGGtkKOsoqrbdVuKHU9i2/6TAKjYkRBCCCGEEEIIIYQQ0rHooW2LG5N7vK2vp0ub4sZ8jBs13JjpCa6wqAQ6nY4TU1Fx44A1asQwuLu6oLC4FE6O9vD18YJUKjXJsUQiEWxsrGFjY220fTIMAxcnRzQ1N3PiEaFDjHYMMnhotVps2LoL5W3GpVycHfHomhVGfe6Sngn0V+HCoS1Ysv4FxF25YYhrNBr8/s/vG5bd3VwQGRqMiLAhiAgdgsiwYESGBcPJ0cGo+TQ1NeHxl36D7XsPdTnrLV/5hcXYtGOf0YsdM3IKcDLuGidma2OD15573OwKG++bOG4UmjUa7Nh7yBDT61ls2HUMv3l+NSRiupyUEEIIIYQQQkj/o79GCTGh6PBAznJ1TR2y84vh7+PR7ba+7g6c5dpGLTKKahDkqTRmioK6lVHBWVa5O0Gv16O4ogY5RRWorKmHRqOFRqcHy7KQyBSQiMWwtZbDx8MF3u7OkErobYz0n8QU7uyrCrkcgX4+AmVDLF14SBDir9/mxC7G38CQQH/4qbwFyorcNy4mCi8/sQrfb9+PguKyXm+fX1SC/KISHDt7mRP38XTjzvQYEoiIkAAo7bhFj3uPnMHh05d6dCx7pS3cnB3h7uIEd1cnuP38093FyRBzd3GCm4sjbG2swbIsMnMKcPridZy5dB2n4q4hr7Ck2+NQsSMhhBBCCCGEEEIIIaQjwyNDOcs3e1HcqPJ0xa3kLMNyTl6BsdIyG23vk6uTPawVMoGyIf1B5eMFlY+X0Gn02b2UdM7MkjKpFEEBvgJmRCxVUko6SkrLOTFnJwc8tvYh2NraCJQVcXZyxLGd3+GRF36NXfuPdrhOcUkZikvKcPLcRU7cw90VESFDEBHWWvAYEdr3osc/v/9fbN19oE/b8mGMYsdmjQYbdh3BgxPxisVivPzMI3B3czFl+rxNnxQLtboGR0+dN8QKSspx+Ew8Fs2IFTAzQgghhBBCCCGDFVUFEWJC7s4OkEokaNZoDLFbSZk9Km50sbeBq4MNSqvqDLGzCUUDprgxr6wOGcXc2ajS80rxqw93oFmj7dE+RAwDTzcnqDxd4OvpihB/L3i6mmfnMzIwJCZzixvDhgTAyopmGyV9E+jvi+mTY3HybOuAkFarw487f8KT6x6Cm6t5D3gMdE1NzZBIxHj64aUoq6hCSVkl/Hw8kJNfhISUDKRk5ECr1XW/ozbyCkuQV1iCo2faFz1GhgQiIrSl4PHPH37Nud3DzRkvP76yXdGim7Mj5PLeXQTDMAwC/bwR6OeNJ9csMk2x49hhCAvypWJHQgghhBBCCCGEEEIGgeiIEM5yQVEpSsoq4NaDGYt828xgmJtfhKamZshkppnpTgjpmTmc5bb3mRBzk5CcylkOCw6CmGbyIn3Q0RiWVqvjFM8SYSgUcmz9+kPMXvEETp2/3P0GPysqLkVRcWmHRY8tMz0Gc2Z6dHSw73RfqelZeP+TL/t8H6RSCdxdXVr+ubnAzdUZ7q4tP61EVoi7cg2nL1xGcUn3jWz7Uuy4/3gcSsoqObGFc6bB10KK2xfOmY7E5DTkFRQZYkfPXUV0eBB8vdwEzIwQQgghhBBCyGBEZx8JMRGWZfHS259wChsBtFvuDMMwmBgdiN1n7hhiNzPKoa5vhtLasgfzWJbF/vicdvHiCnWv9qNnWeQXlyO/uByXbrZ0gA1UeWDyqEgMDw+ERExFZ8R4qqrVyCss5sQiw4YIlA0ZKCbFjoa6phZXb7S+1zc2NmHjtr14+pFVUCrtBMxucCspbx2IcnFygJuLE37/yuOGwfvmZg3SsnKRkJKBhOQMJKZkICElE6mZ/Ioej5zpeLbGf735GtYund23O9ONDosds3Nw+uINnLl0A6cu3uhbsaOzI6aOG45FM8dj4fRY2Cttu9kDIYQQQgghhBBCCCHEEoUE+UEhl6OhsdEQu5WQjFlTup/5x8/bFQyA+2UuGo0G8TduY+K4UaZJtp/V1zfg+u0ETszfx12gbAjpnlpd02620ciwYIGyIZYuKMAPocGBSE7NMMSq1TXYtH0Pnli3EnK5XMDsCMMwmDZxHDzdXXErIRmlZRWorK6GpocNyR90v+jxxNk4TtzT3Q0RoUMQ8XOx4/1/9ko7/OL3f0ZzM/caKhsba7j/XKTo7uoCV8P/neHu5mKIu7k6w15p12Wj1VeefRQsyyI5NQOnL1zGmQuXjVbsyFiJcTLuGmcbf18fzJwyoacPmeDEYjEeWb0M//joc+j1egCAXs9iw65j+H/Pr4GYrrkihBBCCLEYDNPyb7Cjh4AQy0bFjYSYyCc/7MW3O45yYn7ebpg/ZUyP9xEb6Yf9FxKg0bacRNLpWVxMKsGckT5GzbW/NDRrEZ9SijN3i1Bc1WCSY2TkFiEjtwi2Ry5g/IhwTIyJgLMDFQcR/hJTuLM2KuRyBPh6C5QNGSgYhsH8WVNRU1uL5NRMQ1xdU4uN2/fiyXUrez0rHzGO4tJyzrKLkwOnK7FUKkFESCAiQgKxcmHres3NGqRm5vxc9JiJxJQMJKZmIiUjBzpd74seAWDKuBFYs2RWn7btC4ZhEOjrjUBfbzy5emFLsWNuQe+LHcsrse3AKWw7cApSqQQzJ8Rg+dzJWDJrApwdO+/SSgghhBBCCCGEEEIIsSzX79yDTs89/5mRnd+jbW2tFYgM9sPd1GxD7GzcFUwYG9NlwYKluHT1BjQPNL8ViUSIGUrNM4n5SkxO4yzLpFIE+qsEyoZYOoZhsGLxPPywZSfy8ltnhysuKcPWXfuxbtVSmhVUQOUVVdDqdBgS6I8hQQEAgNdfeAIFRSVITE5DQnIqEpPSkJCUiuS0TM7nWU8VFpegsLikXdGju5tLuyLDX7zwBD74yx/6foc6wDAMwkKCEBYShOefeNhoxY5KpR3c3T3g5eUFb29vODs745HVyyASiYyav6mpvD0xb+YUHDh6yhArKCnH5Zv3MGHUUAEzI4QQQgghhBAy2NAZIkJM4ETcDfzqb19wYtYKGXZ/9jZsrHveec5GIUVMqAqXEloH884nFmHmcG9YiSxnMI9lWcTdK8GeS1loaO5ZYYeDjRReTtaQS60gthKBAaDVs9Bo9SipbkBxVQNYtvPta+sbcfTCDRyLu4nJoyOxZM5UyKQS49whMiglJHGLG8ODA2FlRZ3qCH8ikQgPLZ6HH7bsRm5+oSFeUlqOLbt+wnoa1BNEcVkFZ9nN2bFH20mlEkSGBiEyNAhY1BpvbtYgJSPHMNPjvdRMJKRkIDUzt8uiRysrK3z85zcEvYjHGMWOzc0aHDx1CQdPXcJzvxdh6rjhWD53CpbNmQgPV+d+uieEEEIIIYQQQgghhBBjKywuxbInftlu5qWhYUE93sfkUZGc4sa8giJkZuci0N/XaHkKQa/X42xcPCc2PDwA9nY2AmVESPcSU7jFjWHBQTRORXiRSqV4+KGl+HrDFpRXVBnimdm52HPgCFYsnj8gitktUUkZt9mrna0NlEo7KJV2CAsJwvJFcwy3aTQapGfmICEplVP0mJKe1aeix7YFhR7urnj7N6/27Y70grGKHdXqGqjVNUhNTQUABPj7wVmpwIolCxEZEWZRz+k50yfh1t17yCtoLUA+e+U2xsdEWtT9IIQQQgghhBBi2czkDCT78z9j7Y7nvti+zarTsq2+79vqtX3fFuCZN49tAf6PuSXq5HednlOI1a++C52Oe/t3//wVosP8Ddv19BGbFO3HKW6srG3GyVv5mDXCMmZvrKhpwuYzaUjKq+50HYlYhDAfe/i52kLlagtfVxvYKaRd7rdJo0NeWR1ySmuRW1aHpLwqqOvbnzBlWRZnrtxFQmou1i+fg+CA3neVZPU8Xx+8tqcThb3G9/2og4e8qlqN/KJiTiwiNBDtXsl8js33fdiKT/Eu3+eZUJ8BfI/Lo2siY9yOixKpFdY+tBTfbNyGsorWorqs3ALsPnAcDy1pHdRj0PfvGiyv5wnA6zHn8RUJACDi8bW1D6/N4rJqgGktYHZ3dwNEfXn8Wo4tlYsxNCIUQyNCObc2NTUjJSMbCckZLbM8pqQjITkDaVm5sLO1xsfv/j8MjQzvw3HB73upqPPibQZAYGAgAgMD8eS6FS3Fjjn5OH3xOk6cvYKDJ8+juqau0+11Oj1OXLiOExeu4+W3PsSE0cOwfP50LJs7FX4+nvy+E+ua+74tAEbE472Y53d5ft83+LyPC/gZwPvQfN5YBGxQwOf7Au+vhjweMyEHsFkhvxML9D2H4f3B2fdNef99z+c5zue1yfMxE/K1yev9jM/fIHzPn/HJm+c5MD2f5zi/5wrL5zO/o2MPxnNqhBBCCCFkwGhsbMLyJ19HQVEpJ/7Sk2swYcyIHu8nfIgKzg52KK+qMcTOxsVbfHFjSlpmu8KRyaNpFiRivtTqGuTkFXBikWHBAmVDBhJrawXWr16OrzdsQW1tvSF+NzEFdra2mDNjioDZDV5ti/jcXDpvSCqRSAxFgSsw1xDXaDRIy8hBQnIqEpJScS+5daZHrbbn59H++fb/g1Jp1/s7wVNHxY4paZmcYsei4tJu95OZlY233/0n3n73nwgJDsKKJQuxYukijBw+zOwLBMViMRbPm4lPv95oiOUVlSEztwiBvp4CZkYIIYQQQgghZDAxk+JGQgaGmtp6LHvhHVRW13Lib770MFbMmdinffp5OMLPwwHZRVWG2IH4XAz1c4KnkzWfdE3q/myNuy9moVHT8cX6rko5JkV6YFyYG6xlvXs7kkmsEOSpRJCnEkBLocStrAqcSyhCaoG63fplldX48OttmDJuOJbMnkyzOJJeSUzJ4Cwr5DIE+HoLlA0ZqFoG9Zbiqx+2orautTAsISkFdrY2mDNjitkPfAwULMuipM3Mje5dDObxIZNJERUejKhw7sUBWq3WYjohMwyDQD8fBPr5wNXJAcGBKlRW1yAzJx/J6dnIyiuERtPx4CXLsjh/5RbOX7mF19/+N2KGhWHZ3ClYMW8qQoP8+vmeEEIIIYQQQgghhBDSDT7naPn0teB7bphnk48Od8myeOH//QWXr9/hxKdNGI0P3v7Vg2t2uy8RA0waFYE9xy8bYtdv3cXCOdPg4uxkrJT7FcuyOHr6PCfm4eqEIf6qLn+fjFXXDWC7xac5o6BNcIg5SEzmztool8kQ6N/75sFd4/VmyPPYfLbve94sz9cWn8an/JqJtRzdWBwdHLBu5TJ8u2k7mptbm1VevHIdSjs7xI6JeeCw/BrN8nor5NWHjO/7YP+OBReXthkPdev9eKhEIkF4aBDCQ4Pw0GJu0WNqehYSktOQmJyGxKRUw0yPbYseF8yeinUrF6NvrzPjPmYMwyA0OBChwYF47vG17YodT52/1O3Mjimp6fjb+x/hb+9/BH8/XyxfvAArli7EuDGjIBIZt4mysUSEDoGzkyPKKyoNsbPxdxDo1+a6GKEa9RHSHyy2EaEFN4kdlNdA8TmnYKnP0cGKfl+EEEJIb1nGFdOEWIhX//w/JKRmc2JLZ43HW688zGu/K6ZG4d9bzxn+PtHqWWw8nYbXl0bBSmR+f+TVNWrw3YlU3Mut6vD2oX6OmDLUE6E+9hAZ6Y9UKysRRga5YGSQCwor6nEusQgX75VA02YGzTOXbiIhORNPr10ElZe7UY5NBr6EJO5AXnhwIKysBJzxiQxYDvb2WLdyKb7bvB1NDwzqXbp6A0o7O4wfG9PF1sRY1DW1aGzizgDo7tq/F9BYSmHjg/ILS3D9bjLAMHC0t4NjVBjWr5iHOVNiceDEeew6eAqHTsWhobGp031cu52Ea7eT8Md/fo6I4AAsm9dS6BgdEUzFvYQQQgghhBBCCCGEmJEPv9iI77f9xIkF+Hpj2xfvQSLpfZPR2OGh2H/qKrS6lqapWp0OG7btwWvPPW62hQBduRR/A0kp6ZzY5NFRdJ6TmLWE5FTOcuiQQIscryDmy9PDHauXL8SmbXug17dey3HkxBnY2dpiaESogNkNPsWl3CI9d1cXo+1bIpEgIiwYEW1mf21ubkZqRjYSk9OQkpYJdzcXPLp6qdl+PrYtdjx98Tr+/K/PkJ6ejpqaGtTU1HQ5Q2VWdg4++PgzfPDxZ/D0cMeyxfOxfPFCTJk03qzeX0UiESbFjsKeA8cMsRt3U7Fi3mTY2Zhv431CCCGEEEIIIQOH+fyVTMgAsPmnU5zloSF++P6fv+I94Bbo5YTpI4fgxLXWAqvsklqcvJWPWSN8eO3b2KrrmvHJgUQUVtS3u83BRoq1k4MQ6edo0hw8nayxamIgpg71xMbTacgoquHcfn8Wx+fXL0NwQN8fP41Wi4LiMuQXlaG+sQlajRYarQ4iEQOJWAyJRAwXRyVU7g5wsLPhe7eIQIpKypBfVMKJRYYNESgbMhh4erhh9fJF2LhtN2dQ7+ips7CztUFURIiA2Q0ObbuUymVSKO1sBcrGMuj1euw/fp7TfVgqlWDu1Fgo7Wzx8LK5eHjZXNTVN+DwqYvYdfAk9p+4gJrauk73mZiaicTUTLz7n+8QoPLCsnlTsHzuVIwbGWmRFzMRQgghhBBCCCGEEDJQHD97Cb9+59+cmI21Anu++xDOTg592qettQITYsJx5spdQyw1PQvnLsZjyoSxfNLtd5VV1di+7xAnprS1xtjhYQJlREj3qtU1yM0v5MQi2xQlEWIMQQH+WDJ/NnbvP8yJ795/GDbWCgT4+wqU2eDS1NSMqmo1J2bM4sbOSKVSRIYFW+z7y7GzV1BXVwc3Nze4ubnB3dUFQ8OD0dDQiN0/HURuXn6n2xYWFePTL77Fp198CycnRyxZMBfLlyzEzGmTIZfL+/FedGz8mJHYf+SUoVhTq9Ph8o17mDmRGjATQgghhJg70c//Bjt6DAixbFTcSIgRuTgqUVRaaVh+/uGFsLVRGGXfC8aH4W5GEYoraw2xA/G5GOJljwB3O6Mcg6+Kmkb856dElKkb290WG+aGZbH+sJb139uOm4MCv1g8FGfuFmLflRxotK1FQo1Nzfjv9zvxzMOLERkS0KP9FZWUIzU7H7kFxcgpKEFBcRl0bWaG7IzS1hq+nq7w9Wr5F+LvDZm0911rSf/S6XTYc+gkJ6aQy+Cv8hIoIzJYBPr7Yun82djVZlBvz8GjsLGWI5AG9UyqpIxb3Ojm4mS23ULNxbXb91BYwu3uOmXcyHZFoTbWCqxYMB0rFkxHY2MTTlyIx86Dp7DvyBlUVHEHUB+UmVuAD774ER988SM83VywdO5kLJ87FVPGDTerrqaEEEIIIYQQQgghhAwGr//pfU5zPgD44eN3ERXOr1Bh8bTRuJOchYrq1vHQPQeOITIsGC7OTrz23V9YlsXmHfvQ2NjEiT+8eDrkMqlAWRmHTqdDc7MGGq0WWq0OVmIrSMViSCQSiMVWQqdHeEpsM2ujXCZDUACNRxHTiI6KQE1tLY6fPm+I6XQ6bNm1D0+uXw13N9MX2Q12JWXlnGWRiIGLs2kblVs6dW0drly7wfkOFOCnwi9ffh7u7m748L13EX/tBnbu2Y+de39CekZWp/uqqKjEtxt+xLcbfoStrQ0WzJ2FFUsWYt7sGbC1Fabprq2NDUYOi8SV67cMseSMXCpuJIQQQgghhBDSL+hKYEKMaNKoodh+6Jxh+eL1RLzw8AKj7FsqscK6OSPw763ncH9SJK2exWcH7+G1xZHwdhZ2ZkB1fTM+7qCwUWktwbqpQxDpK8xJUJGIwbRhXoj0dcSGU2nILG6dxVGj1eLLzfvw0mPLERyg6nB7jUaLG3eTcS7+FjJyCjtcpyfUtfW4m5qNu6nZAACFTIpxw0MxaVQk3F3oBLG5On/5OoraFOuMHTkMVlY0QEtMb9jQcNTU1uHY6dbPFZ1Oh6279+OJh1fCw91VwOwGtuI2g3nurs4CZWIZ6uobcOJ8PCfm6uyIcSOHdrmdXC7DghkTsWDGRGj/8TucvXwDuw6exu5Dp9oVSj6osKQMn/2wC5/9sAtODkosnj0Jy+dOxaxJo9GPPRQIIYQQQgghhBBCCBm0iku551Bfe2Ydls2fznu/cpkU6xZNwccbDxhiTc3N2LBtD1577nGIRObff/1S/A0kJHGLxMZEhyIqtGfNVs1FQ2MTcguKkJtXiJz8QmTnFaK0vMIwTt2Wq7MjVF7u8PX2MPyzVgg/CxXpmfr6Bpy/dI0TCwsOojFRYlITxo2GuqYWV67dNMSampqxcetuPP3oatjbK4VLbhAobjMW5+zoSA1Fu3EmLh7V1dWGZZFIhLmzpsHd3Q0AwDAMxowaiTGjRuLvf34Tt+8kYOfe/di5dz8S7yV3ut/a2jps3bEHW3fsgUwmw5yZ07BiyUIsmj8Hjo4Opr5bHEPDQzjFjbkFJWBZlhoBE0IIIYQQQggxOTorQYgRTRkTxSluPBt/16gneQK9nDB95BCcuJZmiNU3afHJ/kS8uigSnk7WRjlOb9U3afHfA4kobVPY6KqU4+WFEXBWCj9w5eagwKuLIvHt8RTczmqdkUuj1eJ/G/fg1SdXws/bwxAvq6jG+fhbuHjtLmrrG4yeT0NTM05dvoNTl+8gNMAbk0cPRVSoP6wsYFB2sCguKcPZNoN4bi5OmDh2hEAZkcFo/NgYqGtqcLnNoN6m7Xvx1COr4ECDeiZRXMqdudHd1TI6ggvl2NnLaGxq5sQWzJjQq4sexGIxpk8YjekTRuM///caLt9IwK7DZ7Dr4Glk5hZ0ul1FlRrfbTuA77YdgLeHK757/7eYMYG6hxJCCCGEEEIIIYQQYkoxwyJw+NQFw3KzRmO0fYcF+mBiTATOX0s0xFLTs7B19wGsWb7QrC+uT0nLxI+79nNi9nY2WDlvskAZ9U5tXT3i4m/i8vU7KCwu7dW2peWVKC2vxPU7SYaYm4sTxgyPxITR0bBXCjMLFemZg8dPo66+nhOLiggRKBsyWDAMg7kzp6Kmthb3kluvgamprcXGbbvx5PrVUFCRtMm0nbnR3Y2avXalubkZ+4+c4sRcXZwxc1rHn/EMwyB62FBEDxuKd978LZKSU7F73wHs2ncAV6/f7PQ4TU1N2HfgMPYdOAyxWIxpkydi+ZIFWLpwHjw83I15lzrk6+PFWa6pq0eVuhaO9nYmPzYhhBBCCCGEkMGNqmgIMaLJY6I4y7mFpcjKKzbqMRZNDEdEkDcnVtOgwb/33kV2SU0nW5kOy7LYdDoN+eXcwQ5PRwV+uXSoWRQ23icRi/DU7FCMDnbhxBubmvHFpr1oaGxCQ2MTNu85irf//RWOnYvvUWGjqzUQ5gxEuQIj3RkMdwMiXYBAB0Deg7qO5Mx8fLntCP7v481ITMvp470jxqTT6bDn8EnodHpDTCRisHTedOpWSPoVwzCYM2MKIkKDOfGWQb09qDdB8fVgp9PpUFZRyYm5OVNxY2dy8otwMyGFExsWPgT+Kq9OtuieSCRCbEwU3vvDy0g7vx03Dn+PP772BCJDuu5snl9UijmP/hr/+nIr2M7ahxNCCCGEEEIIIYQQQnibHMttMHb24rVO1uybZTPHwsmeWwx37mI89h46brbn/rJy8vDZt5ug1Wo58bWLppn1DIYsyyIzJx/fbd2H37/7EfYcOtnrwsbOlJRVYP/xc/jDPz7FV5v3ICUjx2x/f4PZveQ03L3HPc8fOiQQgf6+AmVEBhORSITli+bB14d7DUxpWQV+3LEXGiMWzxOutrMwu7u6dLImAYCzcfEoaPP5OGXSeMhksh5tHxYajN/9+heIP3cMWfeu49//+DMmTRjXZdMGrVaLYydP44XXfg2vIVGYNGshduzex+t+dMfF2REKOfd7S06+ca97I4QQQgghhBBCOkIVGoQYUcQQX7g4KlFWqTbEzsTfQYDKo4utekdsJcIzK6bjkx+PIj239QRSfZMWH+1LwPLx/pgQ7t5vXUuvpZXhViZ3hisXpRwvL4yE0lraLzn0hpWIwfppwWjW6jl5VyRYiMAAAQAASURBVKlr8fWWn1BYUo4qdW2n2yulQLATA5US8FUyUNkBCknnj7WeZVFaD+SoWeSqgexqFhlVQEfDduVVNfjvpgMYPyIcy2fHQiHv2UlQYnwXrtxAYXEZJzZh9Ah4ebgJlBEZzFoG9eairr4e2bn5hnhZeQV+3LkPj65ZDolEImCGA0tZRRX0eu67tJsLFTd2RK/X48Dx85yYTCrB7CnjjHYMhmEQHRGM6IhgvPOrZ5Ccno1dh85g9+HTuHo7qd36er0ev/7rZ7h+NwVf/v3XZn3RECGEEEIIIYQQQgghlmpKm+LGhOR0lJVXwsXZ0Sj7l8ukeHTpNHy88QCnEeXRk+eg0+mwfOEcs5rBMS0jC59+swlNTc2c+MRRQxEV2nXTNqGwLIsbd5Jw5PQF5BaYtmhBr9fj+p0kXL+TBE83F8ycPAbjRkaZ1e9wsKqvb8D+o9yZyBRyORbOnka/H9JvJBIJ1j60BN9s3IrSB2YTzMkrwK6fDmPl0gUQiah3vzGxLIviUu71EG4uNHNjZ8rKK3Dx6g1UP3AtkZ2dHSaOG9On/fn5qvCLl5/HL15+HsXFJdh74DB27vkJJ8+cb9ck4T6WZXE+7jLOx13Gc089ho/ee7fHhZW9IRKJoPL2REp6piGWU1CC6IghRj8WIYQQQgghhBDyICpuJMSIGIbB5DFR2HXkgiF25vIdPL58llGPI5WI8cKqmfh06zFk5JUY4s1aPbaczcCN9HKsmxoEJzvTXtCvrm/GtvOZnJitXIyXF0bA3sb8ChvvsxIxeHxmCP67PxFpha2FqPfSsjvdJsQJmKQSYZhry/Y9JWIYuNsA7jYMRnu2xMrqWVzIY3Exn0VtB40G427cQ2J6DtYtmoqIIFWPj0WMo7ikDGcuXuXEXJ0dMWX8KIEyIgQQi8VYs3wxvt20HSWlrR0hc/MLsXPfYaxa1rtBPY1GA7FYTAPTHWjbpdTB3g5yKjbv0OXrd1Fcxm1wMH3CaNjaWAMm6r4dGuSH3738KH738qPIzivEniNn8ePeY7hyM5Gz3o/7TiAxNQu7Pv8LAlSeJsmFEEIIIYQQQgghhJDBalR0BKwVctQ3NBpiZy9dx/IFM4x2jGA/Lzy+Yg6+2X6YM9vfiTNxqG9oxJplC8yi8d/Nu/fw7cbt0LQpRhgeHoRV86cIlFXXqmtq8ePuQ7idmNLleiKRCF4ebvBTeUPl7QVfH08olXaQiMUQi62g0+nQrNGitrYOOXkFhn8FhcXQ6nQd7rOwpAwbdhxE/M1ErFs+D86O9qa4i6SHDh0/g7r6ek5s3swpsLOz7WQLQkxDoZBj/apl+OqHLaipbS0gu5echsPHT2PeLCq4NSZ1TS0aG5s4MXc3mrmxIyzL4sDR02hsbELjz00MGIaBn58f/FTe3WzdPXd3Nzz75KN49slHUVlZhf2HjmLn3v04cvwUGhsbO9zm86+/x+27idix6Rt4eRqv2f59vj5enOLGvELjzOhMCCGEEEJMh2Fa/g129BAQYtmouJEQI5symlvceDb+jkmOo5BL8fLa2fhy50ncyyjg3JacX42/bruFpbF+JpvFkWVZbD2Xgfom7kDdqkmBcFGa/yxJEisRHp0+BH/ecgMaXccFGFIrYLw3g4kqBh42xnsMXawZLAlhMD+IxY1iFmdyWGSruetUqevw300HMGFkOFbNmwixlZXRjk86p9PpsOfwSU4XYIZhsHTedIjF9JFJhKVQyLFu5RJ8vWEL1DWtg3pJqenYf+QEJo8fi4aGRtTV16OuvgF1dfWG/9c3NKCurgG19bWoq2uARquFQi7D/FlTERURKuC9Mj/FpdxiPTdnmrWxIzW1dTh98Ron5uHqjNHDI/otBz8fT7z21Gq88sRKvPvx93j7g684FzndupeO0Yufw4//eQuzJlGBOiGEEEIIIYQQQgghxiKRSDB+dDSOn71siJ25eNWoxY0AMDJyCJqap2PjnhOc+MUr15GZlYtHVi9FgJ8wjULrGxqwY+9hXLp6o91t4UG+ePyhObCyMq+ZxliWRfzNBGzbd4RTmPogsZUVRg4figljYuDv692jAlInB3v4+ngZlrVaLXLyCnAx/gauXL8NjaZ9t9ektCy8+9HXWD5/OiaMjqaLzwSQlJKOO/eSObHQIYE0bkQEY2+vxPrVy/DNxq2cmXCvXLsFpZ0dJsaOFjC7gaWkTbNXmUwKe6WdQNmYt4SkVGTm5KK5ufWzzMPDAwqFAm6uxp3t0tHRAY88vAqPPLwKtbW1OHzsJHbtPYD9h4+i5oHrAwDg4uV4xEyYgZ2bv8X4Ps4g2Zm296umTRE8IYQQQgghhBBiClSpQYiRTR4TxVnOyitGTkEJfL3cjH4smVSC51bOxPYjl3DhJrezZqNGhy1nM3DxXgmmRHlgRKALJGLjDaBdSyvDrUxuAciIQGeMDLKcbm4FFfXQdzKxVKgTsC5SBCeF6YbSJFYMxngxGO3ZMpPj7hQWTW2amF64fg8V1bV4ZuVsyKTCd58d6K7eSkBhcRknNmH0cHh7uguUESFc9vZKrF+1FN9s2m7opqnX6/HBf7/GngPHOBcPdIRF65teQ2MTdu0/Cq1WixHDIk2atyUpLuMO5rm7UnFjR46cvoSmZu4FKQtmTOjVDKLGIhKJ8OZrT2BEZAjWv/Y21DV1htsqqtSY9/hv8NdfP4NfP7eGuvoSQgghhBBCCCGEEGIkk8eN4hQ3nr10rYu1+y52RAREIhE27jkO/QMDe0UlpXj/k68wc8oELJwzrV9ncbx7LwWbd+xDVbW63W3DwgLx5ENzIBGbV+PS7mZrdHZyxKTYURg/ZiRsbWx4HUssFiPQ3xeB/r5YtnA2Ll29ibNxV9oV0zQ2NWPz7sO4ficJ65fNgZODktdxSc81NDRi/9FTnJhcJsPC2TQ7HhGWu5sr1ixfjI3bdnEaEh8/fR52tjaIjuI22WRZtrX5a92DTV/rUFdXD5lMijEjo6Gkwj2O4lLuNRFuLs702u9AU1MTjpw8CwDQ6Vuej1KpFJ6enhCJRCZtkG1ra4uHli3GQ8sWo6mpCZu27MDLv/odGhoaDOsUFZdg6tyl+Phff8OzTz5qtN+htM21SVpNx7MxE0IIIYQQQgghxjRAixs7qVbqj+1ZfffrmGJbofE5QSLkCTKGx6AS0/HF+0NDA+DkYIeKqhpD7Ez8XTyydCZ3cz7H1reeOJKIgIfnjUN0iAqbD11EVQ23Y1Z2aS1+OJmGnXFZiA1zw8QID94zKzZptNh2PpMTs5WLsWpSAK/99qdraWX4/mQqZxAUAGRWwLJQBhO8mX47ecswLbNDRriw2JSgRzK3ZhT30nPxyaYDePHh+VDIZf2Sk0Vj+/4enpCUxll2dXbE1An91AGS72eAnscJZRHPrwMiHu9nPH5f/PE5Nt+8+/7+4ubmhjUrFmPD1t3Q6XQQWVnBxsYamTl5cHFxgrVC0flR26bNAvsOnQQgwsjood0em+XzXOH9PYdH0VwvHu6S8irOdxN3N1d+z3FL1cVrMyM7D3dTsjjfo0YMDYNK5fvgDvp+7D4+zxbOnYErP/li+TO/RWJq6/cUvV6P3/7jc1xPSMNX7/0etjbWne9E39z5bd3h+X7WyVdLk2OFfB/m+77A50ET8n63ezPuT3w+s4X8e1XI3xefjfk8ZjzfFPg8x/n+GSTY60vA56ig76VCvqfwOQcmNV4avcXnfAwARtz3cyqstUP7YIN5zeRCCCGEEEJIT02JjeEs305MRWWVGo4mKFAbGx0GuUyKb7cfhkbbOh7CsiyOnT6P24lJWLV0PsKCg0w6vldRWYX9R051OFsjAIyJDsP6pTNgJUATuK4UlpTh4682o0pd0+42O1sbrFw6HyOHRZqkeZ21QoHpk2IxbeI43ElMxpZd+9sVhSalZeHv//0BLz++Er7e1HC0Pxw6fga1dXWc2LyZU2BnZytQRoS0CvD3xbKFc7Fj70FO/MPPvsGoEVGws7VtKWasr0d9fSP0+o7OUbWeM7t5JxGPrV0BVxfjzrJnyYpL2zZ7tZwm5v3p9IXLqKltea+8/zzz9fWFlZUVJCYsbGxLJpPhycfWIWZENJaueQxZ2TmG2zQaDZ5/9Q1cvX4Tn3zwd8hk/K8patswQqPV8t4nIYQQQgghhBDSnQFa3EiIcEQiESaOGop9xy8aYmev3GlX3GhskUHe+OPTi7Hr5FXE3Uprd3tdoxbHbxbgxM0CBHjYwc/VFipXW/i62sDNXgGRqPOBvrpGDXLL6pBbWouc0jrcy6tCYzO3kGrVpEDYKQS8OLEXbmdW4PsTKe1mbQxxAtabeLbGrjgpGLwcI8KFPBa7klk0P3AOPiO3CP/bcggvrVsIqYTeuk2lqpo7qDttwhiTdtsjpK/8fVVYvnAutu89AKWdHfR6FizLIje/EKFDAnu9v32HjkOv12PUiGEmyNZyNDQ0Ql1Ty4m5u9JA54N0Oh0OnjjHiSnkMsycPFagjLhCAn1xce+XeOJXf8GuQ6c5t23bfwKJqZnY9eXfMcRfJUyChBBCCCGEEEIIGdxYvQmaxwjZ8EIgQjZO5dVMxkIbvnZizMhhkMtlaGxsAtBSaHjuyg0snjPNiEdpfbyjwwLwm+dWYcPuE8gpKOGsVVxSho+/+AHubi6YHDsGY0dFd9kIsDf0ej2SUjNwNu4K7iQmd9gsTC6TYsWciYgdGcG/uNLI2+fkFeLjrzejrr6h3aqjhkdh1bL5vGdq7FlaDIZFhiE40B87fzqMuCvXObfX1jfgw6+34IVHliM4oLfnb3l3auK5vWWpVtfgdmISJxYSFIBhkWG92EvfH3OW1+cgzyaDPF5ffLbl22SQz/YM72Z7fLbv+2M2NCIMNbV1OHLijCFWWlaOA0dOInpoeK8KuGrr6vHd5p14bO0KuPWgiI/X75r3+1H/KC4tb9Ps1QX830sHluKSMly+duuBCAN7e3s4ODgAEKZ5aPSwobh67hjWPv4cjp08zbntq+824k7CPezc/C28vTx5HYdtczGV0RpHCNqoj/Qe/b76F5/XmZBNYnm+Pwj2viDkZ56FnlMgvWexn3t8JofqYFuLfRwIIYQIgSo2CDGBqWOGtStu7A8KuRTr5o/HiDA/bDl8GeXVte3WYQFkFNUgo6i1iEsqFsHL2RoKqRgSKxFEIkCjZaHR6VGubkR5TVOXx/V3t8XIIMvo5JacX41vjie3K2wc68Xg4QgGVl0UefaH+7M4+tix+PS6HvUPNEBLyy7E19uP4tnVc2BlNQhnETMxvV6PmjruzKdOjvYCZUNI9yLDQ1BQVIzzl65CKpWgobERxSVliAwLgbOjA2xsrGFjbQ0ba0XLTxtr2CjksFbIkZyWgfOXrnL2t//ISbAsi9EjowW6R8LLzivkLFtZWdH7QBsXr95CWUUVJzZj4ljYWHcxG2I/s7O1wfb//RV/++/3ePO9LziDi3eTMzBm4VPY9PH/Yd60WAGzJIQQQgghhBBCCCHEsslkUsTGDMOpC/GG2Jm4a0YubuTycnPGG08/hGMXruPg6SvQ6bhFUsUlZdi+9yD2HjyG0SOHYdSIKPh6e0Gh6N0M7DqdDkUlZbiXnIZzl+JRWlbR6brhQb5Yt2Q6HO3t+nSfTCknvxAffrERjU3csV47WxusWbEII6Ii+j0nhUKO9auWYsSwSGzavpczi2NjUzM++W4HXn78oT4UOJKeKiou5SxLJRIsnD3NpLOeEtIXsWNioK6pxcUr18AwDJR2tigoKkZufiGGBPr3al919fX4/seeFzgOZDqdDmUV3M+1luJGch/Lsjhw7BRnVlCpVAJfX1/De6VGqwXLsv3+3uns7ISDu3/EH97+K/757485t12Ov4aYCTOwfePXmDSh7+OgbWdq7M9ZKgkhhBBCSN8woHYlAD0GhFg6+uuTEBOYPCaKs5yWXYD8ojJ4e/TPCcGIQG/86fmluJOah3PXk5GUVdjl+s1aPbKK2xdC9tTkSH5dv/pLVV0zvjqSBK2OW9k4WcXgoTAGIjMasPF3YPCL0SJ8fE2PmubW+N3UbPx0Kh5LZ44TLrkBqrauvqUA5oHngZ2N+RTrENKRcaNG4PK1m4gIDYZIJIJYLMbYmOGYNW1Sxxv83A3Xx9sTVlZWOHPhMufmA0dPgWWBMTGDp8BRr9cjNSMbl6/fQUZ2Huc2V2dHKiZ/QLW6BmcuXuPEvD3cMHJYuEAZdY5hGPz+5ccxIjIU6175E6rUrU0dqtQ1WPj4r/DnXz+L3730GF2wQQghhBBCCCGEEEJIH02OHcUtbrwY38XaxmFlJcLcyaMwLDQAG/YcR05Babt1mjUaXLh8DRcut5zPdHNxhq+PF3x9vODt5Q65TAaJRAIRw0Cj1UKj0aC0vBI5eQXIyctHXkExNBpNl3nIZRIsnzMJ440xW6MJFJWU4ZOvf2xX2Ojv64MXnlwHO1vTz9bYlciwYLz5xsv44octSE7NMMQ1Wi0+27ALv3hqDXy93QXMcOBS13CvC3BzdYZSaX7FuYQAwOzpk1GtViMrJ89Q0F5QVAI/lTckEglnXblcBhtra1grFLCxlqOyqhrFpWWG2+vq6/Hd5h14bO0KuLu59uv9MCdl5ZXQt+kG7ubiLFA25unW3XvIySvgxMaNGoETl24bllmWhUajgVQq7e/0IBaL8Y+/vIWYEcPwxPOvob6+tYl3cUkpps9fjg//+Re8+OyTffqO0tTmu4NEQuPlhBBCCCGEEEJMj4obCTGBYWEBsLezQXVNnSF25sodPLzYdJ1K27ISiTA81BfDQ31RXF6NczdScOl2Ghqauh6I6y1buRgjgsz/RCfLsvjxTDoamnWceKw3g5VhjFkOOnrZMXg5RoQP4/VoeKAx2vG4m4gOC0CADw3oGZP6gdcr0DI4bm2tECgbQnrGzs4Ww4dG4OrN1oGU+Ou3MXHc6C47UTMMg2mTYsEwDE6fv8S57eCxU3Cwt0PIkECT5W0OGhubcDMhCZev30FllbrDdbw8Bu/AZkcOn7rA6dTJMAwWzJwEkUgkYFZdmzctFlf2f43lz/wWd5NbL5BhWRZ//OfnuH4nGd/+64+CX8hDCCGEEEIIIYQQQoglmhI7irN8824yqtU1sO+HQikvd2e88fRKXLiWgNOXb6O4rLLTdUvKylFSVo6rN+/wPq5MKsHY6DDMnhRjlrM1Ai0NPT/+ajNq6+o58dDgQDz3+FrIZTKBMuNSKOR48cl1+HrjdtxOSDLEW2Zw3I7fvfyY2T7GlqxtcaPSzlagTAjpHsMwmD1tMv7z+bdwdnKETCaFRCJBgJ8vpk6KhY21oqWg0VoB8YOzy7F6NDU1YeO2PcjNby1Sq29owPc/7sSja5bDw91NgHskHJZlkZqeZSj8v89eaQe53Dw+F8xBQ0Mjjp2+wIk5Othj1tTxOHn5NtgH6kILikrg7+vTzxm2WrViKcJDQ7B0zWPIyMwyxLVaLV5+/be4ev0mPvvoPcjlvZvBuqCohLNsT58ThBBCCCGEEEL6gfleiUyIBbOyssLEUUM5sbNXbneytum5O9vjoZmj8ddXVuKFVTOwYNJwRAX7wN6254VbYisRVB5OsGpTwBAb7g6Jlfm/lVxJKUVCDndQc5gbsDbCPAsb7/O2Y/DCSBHED6TIsiw27D3JKTAh/LUbyLO1NevnBiH3jR8bw3muNmuaEX/9Vo+2nTpxHKZNim0Xv3nnntHyMzdl5ZU4ePwcPvj8Bxw+eaHTwkaFXI6xI4f1c3bmKzUjG/dSMzmxUdER8PIw/4HfIf4qxO35EisXTG93265DpxG75BmkZuYKkBkhhBBCCCGEEEIIIZZt7MgoSKWtM2fp9Xqcv3yj345vZSXC5DFRePPlh/Ha40sxIiIIIpFpxna83JyxesFU/PWNJ7F64VSzLrrbtu8IKqu5575DggLw4pPrzKaw8T6JRIJnHl2NYZFhnHhtfQM27T4MlmU72ZL0FRU3Ekvj4GCPqIgwuLk6I9DfFypvT5RXVsLXxwueHu5QKu24hY0/k8lkWL9qKXx9vDnx+wWOpWXl/XUXBNXY2IRL8Tfw8Rc/YPPOn5DdZkZCd1fzb2ben06eu4i6em5zgHkzpsDO1gZuLk6ceNvZHYUQNTQC8WePYs7M9s32v9u4BZNmLUJuXn6v9tn2fvl6mf94MCGEEEIIIYQQy0czNxJiIlPGROHAqcuG5bPx/DuB8iWViDF0iA+GDmntHFZd24DconKUVdVAq9VBo9VBp2chFVtBLLaCQi6Fj7sTvFwckJFXgg83HTFsywCYGG7+swdW1TVjxwVuQYadFFgXKYLIAorXAh0YzAti8FNa6+BdcVkVDpyKx9JZ7YuSSN+oa7kzNyrtaBYvYhmcHB0QGRaCu/eSDbHL124idsxISCSSLrZsMWXCWNxLSUNRcakhZm3du+6N5o5lWaRl5uDy9TtIy8zpcl1rhRyjhkdi9PChNJvfz7RaLQ6dPM+J2VgrMH3CGIEy6j1bG2ts+fQviPnfJvz+H59Br9cbbktMzcSYRU9i40dvY8G00QJmSQghhBBCCCGEEEKIZVEo5Bg7MgrnLl03xM5cvIoFsyb3ax4MwyAkwAchAT6oUtfiwrVE3EnOREFJOXQ6ffc76ISjvR2G+Hlh4qhIBPl6WURTzJt3k3D1ZgIn5q/yxvNPPNyjMQMhWFlZ4an1K/Hp1xuRnNY6ppuYmoWL1+5i/KgoAbMbeNoXN5pvoS4h900YNwq37iYalhsaGnHt5h3Ejonpcrv7BY6btu9Bdm5rgVdDYyOOnjyHdauWmiplwZWVV+DK9du4eecemjWaTtcLHRLYj1mZt4LC4nazPIcFByFkSAAAwNfLHcWlFYbbzKG4EQCcnBxxYNePeOvPf8df3/uQc9vV6zcRM2Emtm34ClMnT+h2XzqdDvkFRZyYr7f5XxdGCCGEEEIIIcTyUXEjISYyZSx3tqfkjDwUlVbAw9Wpky2EYW+rgP0DxY5dySnidq7zdrGBs9K8C2BYlsWPZ9LR0KzjxNdGiGAjMf8ByPtm+jO4WcIi94Emq8cv3kJ0eAACfDyES2wAoS6lxJKNGBbJKW6sq69HemY2wkKGdLttQ0MjSkq57+/BgQFGz1EITU3NuJmQhMvX76CisrrLdT3cXDAuZhiGhg3psLvrYHblxl1UtJnhctbkcVAozPs7QFsMw+A3L6zH8MhgrH3pTVRW1xhuq1bXYvGTv8bbv3wCf3j5EYhE5j8rNSGEEEIIIYQQQggh5mBy7ChOcePZi1cFzAZwUNpiwbQxWDBtLDRaHQqKy5BTUIrcwhLkFJSgoroGGo0WGq0WLAuIxVaQiMWwVsjg4+ECXy83+Hq5QeXpBjsbhaD3pbdq6+rx4+5DnJi1QoHnnngYcrl5zdjYVssMjmvw5/c/QbW69dztjoMnETbED04OSgGzG1hoTJRYIjdXF4QGByE5Nd0Qu3jlOsbEDIeVlVWX20qlUqxbuRTfbd6BgqJiQ7y+ocFk+QqFZVmkZWTj8rVbSMvM7nJda4UcE8bGYGR0ZD9lZ95YlsWBY6c4MwZLxBLMndHasMHXyx3xt+4Zls2luBFoaRTw7tt/wMjhw/DYsy+jrq519snSsjLMXLgC//rb/+HVF5/tsllDYXEpNFotJ6aimRsJIYQQQgghhPQDunKbEBMZHh4EOxtr1Dxwwuhs/F2smt+/nUqNqW1xo6+L+c9olZRXjYScSk5stCeDYW6WU9gIAFYiBo9EivDPS3pofz6XyrIsdh6JwxtPLRc2uQGCZm4klqq2tg4Hj51qF+9pN+r0zGzOLHZisRiB/iqj5SeEanUN4uJv4sbdJDQ3d96JVCQSITw4EGNGDIWvj6dFdN0WQmZOPmfZ19sD0ZGhAmXD3+zJYxG//1ssf/a3uH0vzRBnWRZ/+uAbXL+Tgu8/+D19DhBCCCGEEEIIIYQQ0gNTYkfh3X9/YVi+dvseamrrYGcr/Pk1idgKft7u8OtgxqH7xQsD6bzwtn1HUdNmvGvl0vmwV1rGzHzW1go8/NBifPbNJkOssakZm/ccwUuPPTSgfldCYVmWihuJxZo4bjSnuFFdU4OEeykYNjS8222lUmm79xCVt6fRcxRKU1MTbt65hyvXb6O8sqrLdT3cXDA2JhpDw0PMdkZfIVy/lYD8wmJObPL40XCwby2uV3lxv08UFJVAo9GY1eO4YukihIUEY9nax5CalmGI63Q6/OI3f8S1G7fx+cfvQ6HouIFDbpuCTScHO9haW1azB0IIIYSQwYhhWv4NdvQQEGLZaEoSQkxELLbChFHcDmdnr9wWKBvjyC3kFjeqXM1/oONsQiFn2U4KPBRmmV9fvOwYzAvi5p6ZV4ycwlKBMhpY2g3k2Zr/85uQ+voGbNi6C+UV3CJuDzdXhIUE9WgfKemZnOUAPx+zGoDprWp1DT77fhsuX7/TaWGjQi7HxLEj8doz67By8Wz4qbzoooheCPJTWfzjFejnjQu7v8DaJbPa3bb32HmMXfI8ktK67mhLCCGEEEIIIYQQQggBYmOGQSJp7ams0+kQF39TuIR6iGEYiz/P+aCE5HRcvZXIiQ2LDMOYkcMEyqhvoiJCMW7UcE4sMTWLM1MW6bvGxiZotNyxEypuJJai7XgoABSV9OxaidraOuQXFnFiIUMCjZKXkCoqq3D4xFl88Om3OHTibKeFjQzDICJ0CJ54eAWee3wtRgyLtOjxYFO4cSeBs+zs6IjY0SM4MZWXG+eCcb1ej1sJSf2RXq9ERoThypmjWDC3/Tjohh+3YcKMBcjOye1w2+u3uY+DXGbeMz8TQgghhBBCCBk4qLiREBOaMjqKs3z2yh2BMuGvoakZxRVqTszXzIsbK2oacTebe4J7fhADG4nlDlTO9Gfg0Obc4bn4hI5XJr2irmk7c6N5P78JaWxsxMZtu1FcWsaJuzo745HVy2FlZdXtPvR6PVIzsjixkKAAY6bZ75LTstDY2NThbe6uzlg8Zypef/4RzJw8zmK6VQvNXsl9P6xuUwxuqWysFdj4n//D+2++CpGI+2dRckYOxi59HnuPnhcoO0IIIYQQQgghhBBCLIONjTVGDx/KiZ25eFWgbAavY2cucZatFQqsXbHIIgs4H1o8r935++Pn4w2zbZK+a9vsFYBZzLJKSHcS7iVj78Gj7eJDAv16tH1qBrfZq1QihZ/K2yi5CUGv1+Onwyfw8ZcbcOnqTTQ1N3e4nkIuw8SxMXjtucewaul8+Km8LfJzoT9UVddwliePHw2xWMyJyWVSDPHz4cTOxl0xeW594eBgj33bN+LN3/6q3W03bt1BzMSZOHn6HCdeWlaBxOQ0TqyguAxp2fkmzZUQQgghhBBCCAEAcferEEL6avIYbnFjQmo2Ssur4Ors0Od9Nmu0yCspRU5ROXKLylFeXYtmjQ5anQ46vR4SsRUkVlawlkvh5eYIP09nqDyc4aS04XWSMr+YWyRoJWLg5Wzd5/31h/OJxXhwjEtuBYzxtOwTtVYiBhN8GBxIb71j8XdSsWx2LKzl1DGtr1iWhbq2zcyNdjSQR8xXc3MzNm3fi4KiYk7cydEBj65ZDhubnr0/5xUUoqGhkRMLGWLZxY3WCnm7WFhwAMaNHEYzNPaRvR33IpKOLn6wVAzD4PVn1iI6fAjWvPQmyiurDbfV1NZj2bN/wJuvPoY//eLxdgWQhBBCCCGEEEIIIYSQFpNjYzizNZ69eE24ZAahwpIypGRkc2LLFs622AZ/1tYKrFq2AF9+v8UQyyssQWZuAQJ9LbcYyRy0Pb9va2PTo2aZhAgpOTUdO/cdalfgPHViLAL9e1jcmJ7FWR4S6GfRz/2UtExcu9V5E2x3V2eMjRmOqIgQmqGxh+xsbVBb19oQW6vVdbjepLHRSM3KMyynZWSjoLAYXp7uJs+xt0QiEd5587cYOXwYHn3mJdQ88BlQXl6BWYsewnvvvo1fvvI8GIbBuUsdNxLYuOsYfv/SOkil9FwihBBCCCGEEGI65lHcyLKAMbvs8d0Xn+0Ha7dAhsfF3iLhnoaMlZTHxt0XR4yKjoCNtRx19a2FI+eu3cOKeZMBcc8KT/R6Pe6m5+NWSg5yCstRVFYNfQ+fZ3fTW7tn2Sjk8PVyRZCvF8YND4Ojfe9mpVM3FXGWne1kkFiZ70X+Gp0eF5NKOLExXgxkYssvaon1ZnAog4X+56eBRqvF5ZvJmDZumLCJGVM/v5fW1TdAf/8B/fnYSlubPuTR8QnuHuF7n/kUbPEu9uJzbJ6H5oFPh18GfH9ffd9Uo9Hgx537kJtfwInbK+3w6JrlsLOzAXqYX0oat0upu5sL7JXKLrbg87vm+5nRs/uk8vFq95yeMXk8XF2ceB6/jyz1uyHTmrfS3o7zmFbX1gGmLPRjeey7j18rZ0ydiPjDm7D8yV/iZkIq57Y//+d73EhIww8f/gkO9ia8GEjP4/Ul5PsZHxZabGzRHeJZfd+35f0+zoOgjzmfYwt4cQyf37WQLPb1RY93r/H+DBDwPJQVj8ZC0g6+a0st9PlDCCGEEELIA6bEjsLf//O1YTn+5l3U1zfA2lrBe9919Y3IKSxBRWUNmrVaaLUPNHwVi2GtkMHb3QXuLg6DtkHZuUvXOcu2NjYYExMtUDbGER0ZBmcnR5RXtDbgPXv5JhU38tS2uFFp17trBgjpb+mZWdi2ez/0eu75kwnjRmHKxHE92odOp0NamwJwS2/22tDY2C7GMAzCggMxNiaaZmjsA3ulHQqLW68xUtfUdLhedHgw7GytUVNbb4idvRiPNcsXmjzHvlq6aD4unz6CZWsfQ3JK68yMer0ev/rdW7h6/SY+/eifuHjleofbl1ZU4acTcVgxb0p/pUwIIYQQQgghZBAyj+JGQgYoiUSMCTFDcfTcVUPs/NW7LcWN3VDXNSDuVhrO30hBpbqu2/W7U9fQiHvpubiXnouDp68gKjQAk0cPRUiAD0Si7k9qajRazrJUYt5d7G5lVqCmQcOJTVINjJO3DnIG0W4MbhS3Xoh67moCpo6NohPUfdR2IE8kEsHGCAPuhBibTqfDtt0HkJmdy4nb2ljj0TXL4WDfVWFieynp3OLGkCDLHsgDWgae7O3sUP3AgFNOfqFwxY0DgH2bixuq1bVgWXbAfeb4q7xwftcXeO63f8em3Uc4t+0/cQFjFz+F3V/+AxEhlv86IYQQQgghhBBCCCHEmMaPHg4rKyvodC1NIDUaLS5eu4UZk3pWeHKfRqtFek4RcgpKkFNYipzCMpRXqnu0rVQqgY+HC3w9XeHr7YYgXy+4ONr3+r5YmsamZly6docTmzB2JCRiy74URCQSYXLsaOw+cNQQu34nGSvmT4OdTc+aCJP22hbr2NnaCJQJId3LzsnDlp0/GT5b7hsTMxwzp07q8ThVdm4+mjXNhmWGYTAkwN+YqfY7DzfXdrGn16+Et5eHANkMDG2LvavVtR2uJxZbYcKoYTh8+pIhdvnaTSyZNxMKhdykOfIRHhaCy6eP4NFnXsK+A4c5t/24fReuXr+JMeNioeykEfKpizcQHTEEQ3w9+yNdQgghhBDSSwwEnWfEbNBjQIhls+wz2oRYAIWcOztkQ2NTl+tn5pfi9NV7uJGUA53eNN379SyLW0kZuJWUATdnB0waNRTjR4ZDLut8JstmLbe40ZxnbQSAezmVnOVgR8DTduB8bZmk4hY3FpdXoaK6Fs4OJpxRagBT13ALiO1sbQZtd19ivvR6PXbuO4TUjCxO3Fohx6NrlsPZybFX+6uqVqOktJwTGwjFjQCg8vZAdVLrAH1ufhFioiMFzMiy2Su5g3nNGg0aG5vMeoCur6wVcvzw4Z8walg43vjLx5wB89TMXIxb8jS+++BNLJ83VbgkCSGEEEIIIYQQYrlYvXnNtG6xzav45M1zBnM+j5lFz57eNTs7W8QMi8CVG61FdqfjrmLG5FhAJOl2+7KKapyLv4OLNxJQV99+NqqeaG7WICOnEBk5hYZYaJAvJo8dgWFhQ2DVy7FNVq/tfqXO8Nm2l+Jv3kVjU+v4M8MwmDhudL8d35Rix4zAT0dOQvvzOLVWp8PFa3cwe/JYgTNrxQr5uu4DY83cyPL5LOOzLcN3/Faozz1+zxNezSZ5P0f5fG/pe975BUXYvH03NA8UJQLAiGGRmDdzChiwPb5vKWkZnGUvD3fYmqywt3+eY+5urpBKJGjWtDb8VtfUwZsu5e0zeyX3WptqdcczNwLAxFFROHLmsuEzoKmpGXsOHsPaFYtMmiNf9vZK7N7yPf7yjw/wp7/8g3NbanoGcvLyMWvWLKhUKgT4qZCbX2j4DGZZYOPuY/j9Cw9DKu3+u13H+L4fCfSZa2Gf9cYzWO+3UCz1/VvIvC30vAAfvN+PLPV5ZqkEfB/l81zh9bdHB387mNN5UEIIIWaPihsJMaHaugYcOXuVExs3IrzDdRuamrH75DVcuJna7X4ZBnB3UMDX1RbeztZQSMWQiEUQMQw0Oj00Wj0qapqQU1qL3LI61Dd1PoBWUl6FnUfO4+Slm1i/eDrCglQdH9PC/rjJKeMWq0W7WVb+3Ql2BKzFQP0Dv9rcwlIqbuwjdW3bgTzqUkrMS1W1GsdPn0dichonLpfJ8Mjq5XBzden1PlPSMtvF8guL4e7mAqm082J3S6Dy8sDdpNbP09yCIgGzsXx2tjZgGIZzkYa6pnZAFjcCLRcJvPbUakRHBGPVC39AWUWV4bbauno89NzvsP1/f8WK+dOES5IQQgghhBBCCCGEEDMzPCqMU9yYnpnb5fp6vR6Jqdk4e+U2EtOyTHLdeHJ6DpLTc+CgtMXE0dGYMGpYu2Zulu7qrUTO8tDwEDg7OQiTjJHZ2tggJnooLl+7aYjF37pnVsWNlsZYxY2EmFJRcQk2btuNpmZuYePQ8FAsmjuzV8WeLMsiJZ07JhoyxPKbvYpEIvh4eSIjO8cQyy0oRHjoEAGzsmy9KW50dFAiOnwIbia2jkefuxiPkcMiERocaLIcjUEkEuGt372BEdFRWP/UC1A/cD+bmppw4MABjB07Fs8+tgalZRWcGZRLy6tw4dpdTIsdIUTqhBBCCCGEEEIGOCpuJMSEDp25gsam1hOuYrEVFs8c3269e5kF2HTwIirVde1uu0/laoPRQ1zh52YLHxcbyCRWPcqBZVmU/1zomJxXjfjUUjRr23fDqKyuxccb9mFCTASWzZrQbsZJiZh7PI3OfDtqNGt0KKqs58RUyoFV3MgwDFRKILmiNZZTUIrh4eZ9otRctZ25UWlLA3lEeNXVaiQmpyEhKQV5HRTnSSUSrF+1FJ4ebn3af3lFZbvY4RNncPrCJYwcNhRjR0XDXqns076FZtem22ptXX0na5KeEIvFsLWxRk1t63tldU0t3N16X1RrSabGjsTVA99ixbO/w7U7SZzb3vjLf7B0zmRYWfXs+xghhBBCCCGEEEIIIQPdhcs3OMtenp2fu07JzMOP+06gpLyqR/sWi8XwdHeFQi6HRCKGSCSCRquFRqNBeUUVqqrVXW5fpa7F/hMXcPDURUwZNwKLZ02EzMKb/AEtBaI5edzxg9jRA6vgIHb0CE5xY0FxGZqbNTxmjRrcqLiRmLvSsnJs2LobDY3cWXzDgoOwbOEciES9m72zvKISFZVVnJiPlyffNM2CytuDU9yYk1cgYDaWr23zA3VNLViW7bSYdtncyUhMzUSzprUj+cZte/CHN16CXCYzaa7GsGj+HFw5cxTzl69FRmaWIc6yLC5duoT3//0ffPfFJ7h5NxGZ2XmG289euY2p44bzm1GWEEIIIYQQQgjpABU3EmJCOw+d5SxPjx0BJ4fWQpHuZmsUWzGIGeKCyZEe8HPr24x8DMPARSmHi1KOkUEuWBrrhysppTiXUISiyoZ261+4lojEtJx2szhKJNy3i2aNrk/59Ie88npOd1cGgM8AnNBQpWSQXNF6R3MKSwXMxrK1H8ijmRuJMLoraLxPLLbCwyuXwMe774Nvgf6+nAsC7mtsbELclWu4dPUGwkOGYNyo4VD5ePX5OEK4l5rBWXZ1dhQok4FDaWvDLW5U13ax9sDh6+2Bszs/w4u/fw/f7zhoiGfnFeHo2SuYNy1WwOwIIYQQQgghhBBCCDEPSakZSEhO48SWzZ/Rbr3GpmbsPXYBZ6/c7nJ/vj5e8FN5G356urt22WhMXVOLnLwCw7/0zGzU1bcfB9Xr9TgVdw13k9KxfvlcBAeoOtib5Sguq2g3s1mAn2Xfp7b8fb3BMAzYnwd/WZZFXlEJAn29Bc7MMlFxIzFHWq0WaRlZSEhKRXJqBpo13Pe1IH8/PLRkfp8aTmbn5reLbd6xF5FhIYgdPbLPTWTNQdsizeKScoEyGRjs7bgXFWl1WtTXN8DGxrrD9V2cHLBkzmRs33/SECuvrMLu/UexdsUik+ZqLCofbyxZsgR79u5DZiZ3htNNW3diyqQJWDJ/Fj787FtDvKS8CskZuQgL8u3vdAkhhBBCCCGEDHBU3EiIiTQ0NuHAqUuc2PK5kwz/Ly6vxn+3nUB5VfviAKlYhLkxPhgf7g5buXG7TiqkYkwZ6onJkR5IK1TjQHwu0gq53Uzvz+I4d1IMFk4fC4ZhoLTlnrArq2mCRquHRNy7znj9IbeM+5i62wAy8cDrGuarZAA8UNxYUNpl5zjSOf2D1bBoef0S0l/U6hpDQWNufmG361tZibBm+SL4+/rwOm5ocCDWrliM0xcuobCopN3ter0eCUkpSEhKgcrbE4vmzoCrswOvY/aHpqZmJLUpbhwWESJQNgOHvdIW+Q88T6rVNQJm078Ucjm++dcfcTspDTfuphjin2/cTcWNhBBCCCGEEEIIIYQA2Ln/OGfZ090VsaOiObGUzDxs3HMM5ZUdz7KokMsRO3oEJsWOhrubS6+Or7SzxdDwEAwNbzkXrNFocP12As7GXeHMNnRfaUUV/v3VFkyNHYklsydZ7CyOOfncJon2SjvYKwdWx1epVAoPN1cUFreen87JL6bixj5oampqVwxLxY1EKFqtFumZ2T8XNKa3e27e56fyxpoViyAW9+3yto7e33U6HW4n3MPthHvwU3kjdvRIhAwJ7PWskEJ78H0RAORyy/wsMxe2tjYQiUTQ6/WGWLW6ptPiRgCYPGY4bt5NQWpW63eNcxfjERYciBHDIk2aL196vR4bt+9FY1Mz5syZg+vXr+PKlSucdT7+7Cs89dg6eLq7orC4tdn6ufjbVNxICCGEEEIIIcToqLiREBM5eu4q6uobDcsikQhLZ00AAOQWV+KTHRdQ+8Dt94V4KfHw1CFwUcpNmh/DMAj2sseri5U4l1CEvZey0azVc9Y5fO4aahsasXr+FPh4uODBUjq9nkV+eR383c1vgCy3tI6z3FIEOPD4KrnLdQ2NqFTXwsne/H4n5k7l5Y67Sa0dhVPSs6DX6y1uAINYjt4WNAItF3aEhQRi/JgYuLo4GyWP0OBAhAwJQHZuPi7GX0dKWqah+/GDcvMLsWXXfrz45No+dUXtT0lpGdBotYZlkUiEyNAhAmY0MLS9IKa6ZnDM3HgfwzB4bt0yPP+7fxhi+09cQF5hCXw8LberLyGEEEIIIYQQQgghxrDrALe4cen86YYxFp1Oh52Hz+HM5Vsdbuvj5YEpE8Zi1PAoyGTGKcyQSCQYGzMcY2OGIyevAOcuxuPytVvQPnDuGABOX7yOhOQMPLl6Efx8PIxy7P6Um8cdX/D18RIoE9Py9fHiFjcWFAuYjeWqb2h/bYKlFvYSy9TTgsb7fLw88fBDSyCR9L0heHjoEAyPisDNO4kd3p6dm4/s3Hw4OTpgbMxwxAyPgtjK/K8vYVkWtxOSOLHQIYECZTMwiEQi2NnacBq8Vqtr4OXp3sU2DNYvn4N3P/4ezZrW7xjfbtoBuVyG8BDzHKNmWRabd+wzvC4YhkFMTAzCQobgh42bDevdSUjE5fhrmDx+DLbuPmCI307KQGV1DRzp2iRCCCGEELPBMIDI/P+UMTl6CAixbFS1QYiJ7Dp8jrM8aXQU3FwckVVYgQ+3nGlX2CgVi7B6UiBeXhRp8sLGB4kYBlOGeuL3q4Yj2EvZ7vbzVxOwYc8JSCUSuLk4cm7LLatrt745qK7nngT3HKANJ50VgKTNu3h1Tb0wyVi4kEB/znJ9QyPyC9vPZEcIH2p1DS5fvYlvNm7DB59+jcMnznRb2KiQyzFiWATWr1qKN155BkvmzzZaYeN9DMPA39cHa1csxivPPoaxMcMhlbYfJKyorMKdxGSjHtsU7txL5SwH+am67KhJesa+Tfdm9SArbgSAtUtmwfaB55Jer8dXP+4TMCNCCCGEEEIIIYQQQoSXkZWLG3fucWIrFswCADRrtPjixwMdFjZKJBKsXDIfv/3F85gwNsZohY1t+fp4Yd3KJfjDr15EoL+q3e2lFVX48OstSE7PNsnxTantzI0DubjxQW3vN+kZWxtriK24/c8zc9rPbEqIMWm1WqSkZWD3/sN47z+f48ed+3A74V6XhY0Mw2BoeCjWrVwKmUzG6/hWVlZYumAOXnr6UcRER3U6A2RFZRUOHT+NzTv2dtgI1twUFZeirKKSExsWGSZQNgNHu2avDxQ6dsbFyQFL50zmxLQ6HT7/9kckp2YYNT9j0Ov12Lb7AOKuXOfE5XIZ/vW3txEY4M+J/++r7zAmJppTDK/Xs4i7ntAf6RJCCCGEEEIIGUSouJEQE2hu1mDf8ThObMW8ySgorcanO86jsZnbFdTX1Qa/XzUckyI9IGKE6RvgopTjlUWRWDHeH21TuHI7GdsOnoXKw4UTzyk1z8IGTZsZKKXmPclYnzEMA0mb+6bV6oRJxsI5Oijh5uLEiSWnZwmTDBlQampqDQWN//7sGxw6fho5eQVdbiOXyTBiWATWrVxiKGgcEujfLzMmOjk6YN6sqXj9xacxe9ok2NrYcG4/e/Eq9Hp9J1sLr7auHulZuZzYsIgQgbIZWJRtihur1eb5HcCU7GxtsG7ZHE7s6y372nV7J4QQQgghhBBCCCFkMNl18ARn2dnJAZNjY9DYpMGnm/bjbkpmu22GBPjhD796EdMmjTPM8Ghq7q4ueP3Fp/D/2bvr+DjuM3/gn1kSrpiZWbYkS2Zmpji2EydxnDRJMYW70vXaXq/t/dq7wl2a9C5pk4YTx8zMlkG2DGJmZpZWC/P7Q/ZK313xrjS70vPuS690np3ZeZZm1/Od5/k+s3ktpDrFLYpeJd75+BDSsgsmJRdjqW1oZJZ9vT0FymRi+fqwj6u2oUmgTMybVCpFoL8PE8stML3CG2L+1Gq1tqDxD395D58fPIbHGSMXNAb4+WD9qmX4p2+9hh1b1sPKynhNwV1dnLFp3Up8/xuvYvmi+XpjoE8VlZQh3wyuE0jLYmdtdLC3m7LfAZPJXq5T3Ng+cnEjAMyOi9KL9SqVeOf9T/E4I3uQLYShVqvx8f4juHYrhYlLxGK8vnc3XJwc8fq+F5nbvjp8HN1d3Zg9ayYTzyui4nhCCCGEEEIIIcY1eEsqQohBLt16gNZ2dlbDZfNm4e0DN9DZw56wDfWywxtrI2FpAhV4Io7DshlecLCR4cNL+VBr+jvS3bifgYggdrDDVIsbVWq28EZ3dsOpRCYCBs7VqKQCi3ELDw5A3YDB0NyCEqxcPFfAjIi5am/vQHZeITJz8lBWUTWq7p6WFhaICAtGdEQoggL8JqWQcdh8LC0wf84suLo44bMDx7Txp7M3zoyJFDC7oWVk5zPPt0wqRXhIoIAZTR32djozN3Z0QKPRTNqFR6bijT1b8e6nR7TLlTX1OHX5FrasXjzMVoQQQgghhBBCCCGETF2HTlxglreuWwGeB9798jTyS9hmf1KJBFs3rMaSBbMFObcoEomwYvF8xESG4eMvj6C4tL9Znkqlxt++OIZvvLgdUaHmcV65t1fJLMttBy/WMXdynSIktVoDtVot+FiKOQoPCUJ+UYl2uaColJ5LYhRqtRpFJWXIzMlHTl4hehSKEbfhOA5+Pl6IjghDZFgI5DqNNieCjY01Fi+Yg/lzZiEzOw+37z1ATV09s8615LsIDQ4AJ1Bj8pFoNBqkZ+UxsRlRESabrznRnbmxrX1010RV1tQPGlepVPjbx/uxetlCrFu1VK+5wmSqb2jCJ18dQUERO1O1SCTCqy/uRERoMABg34vP4ee//h2Uyr7fGD09Pfj48/1YsXwZbty+p92uoqYeGg0PkYjed4QQQgghhBBCjIOKGwmZAIfO3mCW58ZH4UJqEVo7e5h4hI89Xl8bAZnEtAYL4oNdIJOK8d7ZHKbAMUen81ZVYxfqW3vgam+8jnnGMHIZ0dQ1nR+7ocKDA3Dj7gPtcn1jE5qaW+HkaC9gVsRcdHR0Iiu3YMwFjeGhQdqCRomAgxlDCQkKgJeHO6pqarWx67fvITYq3CSL2tJ0BvIiw4Igk0kFymZq0R3M02h4dHR26c3oONXFRYdhTnw07j7M1Mbe++woFTcSQgghhBBCCCGEkGmpoqoGdx+kMbFt61fgg4MXkFdcycStLC3xzVf3IDjQfzJTHJS7qwu+/419+PDzQ3iQ1n+uT63W4N3PjuLNfc8iyNdDwAxHR7fpqSmOMxjDYI9LqaKCvPEICw5glnsUCpRVVCHQ31eYhIhZM6SgMSo8FFHhoZNS0DgYiUSCmbFRmBETiXsPHuP0hSva2yqra1FQVILQYNMsdC8uLUdHJ9tsPTYqTKBsphbdcc/WttHN3FheVTfkbRqNBmcvXUdaZg5e2r0dfj5eBuU4VhqNBtdupeDYqQvoVbJNEUQiEV5+/hmmsbGbmyu2b9mA/QePamPvfvAxXn7xeWbbHkUv6pta4O7iOKH5E0IIIYQQQgiZPqbm2W1CBKRSqXHsQjITi4+NQn4526kryF2O19aYXmHjU9F+jnh5RSg+uJiHgTU6HAftMg8gOasGW+cFCJHikKRituBGqRlixSlA97HJpuig5WTw9nSDjbUVOru6tbG8ohLMnTVTwKzMB8/z06obJM/zqG9oRElZJbJy81FaXjmqgkYLCxkiQoMRHRlmsgWNA3Ech6UL5+Dzg8e1scamFmTm5CM2KlzAzPQ1NDajqpYdOIqNpIE8Y7GxtoJYLIJ6wOzITc2t0664EQBe37OVKW48e/UOHmflIybUhy6mIYQQQgghhBBCCCHTyuFTl5hlezs5etQipOUWM3FbG2t85/W98PX2nMz0hiWRSPDKC8/C4qAFbqf0N79UKlV497Nj+Ndvvwi5rbWAGQ5Po+Gh0bDjEpIpen5SMsh4ukqtFiAT82dnJ9draplbUETFjWTU1Go1ikvLkZmTh5y8InT39Iy8EQA/H29ER4QiMiwEdjoNNYXEcRwS42fgzv2HaGpu0cavJacgJMg0Z2/UnbXR090Nri7OAmUzteg2ex1tcWNZVS2z7OrijPqGRiZWVVOH/3zrPaxZvghrVy6ZlFkch5qtEeibTfu1vbsRM8h4+tdffZkpbszJzcfjtHTY28mZ56SsqpaKGwkhhBBCCCGEGI1pX1Fvjgw6sWXgDEicAYMVnGHzvXGGbC/kycAJeL2u3X2IxuY2JtYjssHAuQ0dbWX4+vpIWEhNe4ApPtgFWzt6ceR2iTamW7tzK6cO65N8TapIUyphX5veKTq2xfO83mMbbHCPjA7HcQgL8sfDjBxtLLegBHMTZoz+TkZR3DZMAuPftu8ODNx+7Lq7e3Dn/iM8yshGe0cHHO3t4ebqDDcX5yf/dYKzk+MEzvA3eY+5o6MTldW1qKiqQWV1LSqra6Do7R3VthYyGcJD+mZoDA5kCxp5jekfoEKC/OHh7orqAYWD12/fQ3RE6NheW87Q3znD35yeW8jsw9bGGkGB/oBINPLGE8mQ30iGHFOMjOMAB3t7NDa1aGOXk+9hn5/vEAO7huRuwLYiA/95M4rNd21dj+//6r/R1t7XFZfnecSvfQlisRgerk7wcneFl7sLvDye/NfdFSsXJcHXy32k5MefN2/AscTg95khXSQMPC4I9Bkx+CtbyM+2If9mNMgU7jYyjNE0PpiSeDN9vafl62XoYza9i7tGx9DHPR3fK4QQQgghhAzu8KkLzPLyRXNx5Q47k6OlpQW+/dpLJlXY+JRIJMKeHZuhVCpx/2G6Nt7R2YX9Jy/ja7s3Cpjd8EQiDiKRCBpN/7/Dp2rBn0pnhkpg6hZyToaw4ACd4sZirFm+2CSLuIhpGG9Bo6+3F6Ij+mZoNKWCRl0ikQiL5iXh2On+77SKqhoUlZSZxGzDAymVSmTl5jOxGdGm1ZDWnOkWN3Z0dkGtHnmmYN3ixqUL5qBHocCp81eY72mNRoMzF68h5UEaFs9LwrzZCbC1MX4jhbKKKly/lYJ7D9L0ZnkGAGdHB7y8ZweCA/wG3X7JovkIDwtBbl6BNvbu+x9j0eLFSM/K7d9PVR2SZkQYPX9CCCGEEDJ2HMx39N6Y6DkgxLxRcSMhRnbo7HVm2cfbE5ZW7MmoPUtDYG1hHh+/ZTM8kVbShMLqtkFv71Ko8LCwEXPC3SY5s6HZWUmZ5dpOgRKZYM09+jM3ym2shElmiggPDmCKG0srqtDd3QMrK8thtpp+Oju7cPveQ6Q8SEOvUqmNNza3oLG5Bdl5hdqYWCyCq7PTgILHvv/a28lNdpBUpVKhurZOW8hYUVWDltbBj4FDGa6g0dxwHIcl82fjyyMntbH6hiZk5xUiOiJUwMz68TyPtMxcJhYz1uJLMqLo8BBcv31fu1xWUY2UB2mYM81muLW2tsKqxXNxSKcrvVqtRmVNPSpr6vW2kUjEOPfZW1g2f9ZkpUkIIYQQQgghhBBTxHH6XXOEbLQxzRr2GWW/QjV0McGGLLX1jbhx5wETs7S1h2ZArmKxGN/Ytwd+Pl6Tnd6oiUQi7N29HV1d3cjK7b+I/2FmPh5mFiIhdhxFIwY1YRv9e1QmlaBH0d+IceCYzVTSq9QvzGBnvDK9z4cpCw8JwtXku9rl5pZWNDQ2jm3mN4OOSeb3eimVSqRn5aC6th6WFjK4ubrAzcUZzk4OJj8GyI/je6urq/vJOGmNdry0R6EY1ba+3p6IDg9FZHgIHOztBiRi2sXXM6LCcD35Dppb2sA/eY9eTb6LQP+hmnwKI7egmDnWcxyHmAj9mffMi+k0YNMtbuR5Hlm5BYiNGvq3QK9Sidp6dpbGAD9vBPr7IjYqHB9/eRgVVTXM7Y1NzThy6jxOnLuMWTNjsHh+EgL8fAx6rymVSqQ+zsT1WykoKasYcr3F82dj64ZVsLSwGHIdjuPwxit78YOf/FwbO3j0BEorKtHe0QVra2vY2NiAV/UgMsgbXh5uo7v+w9Dfs+b63WOCv+OJKTLX94mQ35HTcd8Gd2E2ShbjYkK/pyaN2R7/p+FrRQghxGSY9lk2QsyMRqPB0fM3mZidvSOzvCDKHRE+DpOYlWFEHIcXlgbjPw48hlI1+Inv65k1mB3majIndX1cbHA3r7+woKzNXP+hMLxSnVora0sLODuYbsdDcxDk7wOJWKztbKvR8CgoKUdspGkUcAmN53ncuH0PN27fH7TD32DUag1q6hpQU9fAxC1kMri6PCl6dHGCu1vfIKDNBHQmHA7P82hqbmEKGWvq6pkOiqMlk0q1BY0hQf4mP5g5FuGhQfBwc0XNgNkbr91KQVR4iEkc+yuqatCsU4BKXUqNb+GcBKRn56O5pVUbu3j9NkKD/OHk6CBcYpOsqbkVXh59v3tGOyubSqXGt//1v5B+8XMquiWEEEIIIYQQQgghU8KR05eY82OWlhawtWPHRdetXILQ4IBJzmzsxGIxXtq9Hb/+r7fR2dWljX95/AJCA30gt7URMLuhWchkTHFja2u7gNlMnLY29nFJJRKIxXSedbw83F1hJ7dFW3uHNpZbUDy24sZpore3F6mPMpB8NxUdnfodlUUiEZydHPSavDo62JvNWIBKpUJNbb12nLSiuoYZBxoNHy8P7QyNusVh5kIsFmPRvCQcP9Pf2LKsogolZRUI9PcVMDNWWmYOsxzk7wu53FagbKYeS0sLuDg5oqGpWRs7c/Eagvx9h7yOobOrR692wc2173jq4+WBH3/3DZy9dB1nLl7TuwZBpVLhbuoj3E19BDdXZwT4+sDP1wt+Pl7w8fIYsgCR53m0tLahrKJK+1dcWoGu7u4hH5uzowNe2LkV4aFBo3kqsHfPLvz45/8O5ZNiWqVSiVu37zLrXL4M/N/f/gEAcHSwx5uvv4Rf/PBbJnH9ACGEEEIIIYQQ8zN1rronxASoVGrU1DcxsaysLCgUCixcuBDebg7YNjdAmOQM4Gpvhc2z/XDoVsmgt5fWdeB+QQOSQl0nN7Eh+LmyJ29rOoFeNQ+ZeGqdQCvXKdr08zKdAlNzJZNJEeTvg7yiUm0sr7CUihufSM/KxeUbd4xyX4re3r4BMp0uhTbWVgjy90VEWDBCAv1gMUzHwPHq7e1F6uMMFBaXobK6Ft09PeO+L3s7Ofy8vbQzNEql0pE3MkMcx2HxvCR8dfSUNlZX34jsvEJEhYcImFmf7LwiZtnZ0QGe7qbxnTSVyGQybFm7HB9+eUQbUypVOHb2Ml7evW1afAfxPI/j56/B0d4OW9cuxfU7D9DU0jaqIsfs/BIcOn0Fz25cMQmZEkIIIYQQQgghhBAysQ6fusgs+/j4Mk3/fL09sWb5oslOa9zs5LbYtW0DPvjsgDbW0dWNL09cxGvPbREws6F5uruidUCBWnllFRJmRguY0cQoq6hilj3dXQTKZGrgOA7hIYG49zBdG8stKMbCuYkCZmVaVCoV7t5/hFspD5iCZ10ajQb1DU2ob2hCZk6+Ni6VSuDq7KQteHza8FUutxV0LIXneTS3tGoLGSura1BdWw+1euyzKk6FgkZdM2MicS05BS1t/Q1VryWnmExxY1dXNwqKS5nYcDMKkrHjOA7LF83DV8dOa2Nd3d04deEKdm7dMOg2gzWFlg24ZkAsFmPD6mWYGROJzw8cQ0l55aD3U1ffiLr6RqQ8eKzNxc3FGdbWlpBIJBCLxVApVehVKtHc0or2Dv2C68GIRCIsmpuILSPM1qj3uJRKREWE4XF65qjWb25pxa/+8y+YER2ObRtWjXo/hBBCCCGEEELIU1TcSIgRSaUSzEuIxu0H7MmdwsJCVFRU4BfffBYWUvPo0KdrSawnHhY1oqhm8I6fB24WI9zbHnbWsknOTJ+Piw04AE/LDDQ8UNkOBDoImNQE0J2R0teTCnmMISzInyluzC8uhVqthlgsFjAr09DQ2DzkbcsXzYWd3Ba1T0661zU0jvqE+kCdXd1Iz85DenYeJGIxggJ8EREajPCQQKPM6tjY1IwvDp1gui2OlkwqhZeHO3y8PODt2fff6dQJMzI8BG6uzqirb9TGriWnIDIsWPCiNt0C1c6ubnR2dsHWRLtpm7MAP28kxccyFz2Ullfh3sN0zE6YIWBmk+NBejZKyvsupImNDEVsZCiS4qKQEBuJqqpqVNXWo6q24clfPc5euY3Kmv7ZpH/71j/wzPplZtOxmRBCCCGEEEIIIYSQodxMecgsV1VVoaioCEFBQRCLxXhx1zazG1uZFReDB2mZeJSepY09zMhDXnE5wgJNo7hlID8fT+QUFGuXdYsAp4qySvZx+Xm5C5TJ1BEWzBY3VlTVoLOzyyhjceaO53l8duAYikvLx30fSqUKVTV1qKqp67vPJ1cuWFpYaAsefb09ERoUAGtrK6PkPXgeSpSWVz4pZOwraBxuZreReHu6IzoiDFHhIXCwtzNipqahb/bGRJw4d1kbKymvQElZBQL8fATMrE9mTj4z859UIkVkWLCAGU1NURGhiM4NZQqWs3ILkJmTh+iIML31VSr94uCBzR6e8vHywA/ffB1FpeW4fisFDx9nQjVMYTHP86itbxjnowAc7O2waF4i5s+eNeYCZI1Gg2OnzmLp4gXIKyhC9xiOG7/+wzvYun6l4NcPEEIIIYQQQggxP1TcSIgRcRyHQ//773jlR7/H2WspzG0KhQI/+/OnuHrnMf7v5/sQ4G1ehWgijsPaWT7466nsQW/vUqjw5fUivLYmXPCTVBZSMdwdrVDT3H+CrayNR6DD1Dl5xvM8ytvYmB8VNxpFWLA/MKDZcI+iF2WV1Qg0gQELocVEhuFu6mMoenv1brt84w5mRkdg5ZL52oK/7u4e1DX0FzvW1jegrr4JPQrFqPanUquRV1iCvMIScBwHX28PRIaFICI0CI4O9mPOv7C4FAeOnRnV/jmOg6uz05NCRg/4eHnA1cVpWhckcRyHJfNn48CxM9pYbX0D8gqKER4aJGBmQFxMBB6m938/9SgUuHDtFnWFnCCrlsxDflEpWlr7v4guXr+N0CD/cX02zUVbewfOX2Nnr7W3s8WqxfMgk0nh6WKPWYhgbj9w8hJ2feNn2uW07AIcP38DW9cumZScCSGEEEIIIYQQQgiZKA52ctT09J9v7+rqwrlz5xAUFIRf/fwn8PHyEDC78eE4Dru3b0R+YQkzW9v1Ow9Ns7jRm32OyyqqwPO84GO1xlZWrlPc6E3FjYYK9PeBTCpFr1IJoG/sOb+oBHGxUQJnJryW1rYhCxu9PNxga2uDuvpGZoxktHoUCpRVVKGsogr3H6VDJBLB38cbEaFBiAgLNuoMiHkFxTh2+gI6hpl5cjhSiRReHm7w9nKHz5OxUrspMkPjcOJio3DtVgraBsyKey05xSSKG6tqapllua0NpANmCCTGs37lUhSXVjDFwKcvXEOAr8+oisCH+i7mOA7BAX4IDvDDjk1rceveA9y4fR9NzS1Gyz0iNAiL589GbFT4uJtM3Em5j6rqGjjY2+N733odxSVlcHV1Ro+iF9eT76KrqwudnZ3o7u7WK3x8lJ6Nk+euYNPa5cZ4OIQQQgghZJQ4ru9vuqOngBDzRsWNhBiZh6sT3v+vf8GLP3kLycnJUOgU0Vy4nY7Y7T/Bb9/ciW/tXgWx2HwKZSJ8HOBiZ4mGtp5Bb08racL9ggYkhQpfZOfnassUN6bX81jiJ2BCRlbcCnQq2Zifl/DP+1RgJ7eFl7srqmr7Z9rKLSil4kYAbq7OeH3vLpy9dB35A2a3fOpxZg6y8wqxaF4i5iXFw8rKEv6+3vD39dauw/M82ts7UNfQxBQ+1tU3jtiZsKyiGmUV1Th3+QbcXZ0RERqMiNBgeLi7DjtQz/M87tx/iPNXboLn+UHXsbWxYWZk9PJwg4WFxRienekhyN8XYrEY6gGvVWFJmeDFjf6+3pgRFYa0rDxt7HFmLuJjoxDg5z3MlmQ8ZDIZtqxdjo/2H9XGenuVOH72Cl7atWXKXTgD9B1HTl9KhkLBFndvWrUYMtnQA8fPrF+GyNAAZOeXaGO/eesDbFmzeEo+T4QQQgghhBBCCCFk+vj47f/A9n3fQ0cnWzhTVFSEN7//I6gUPdj7wm6zOw9mJ7fFulVLcHBAo79HWfloaeuAg52tgJnp8/P2ZJY7u7rR1NwCZydHgTIyvq7ubtQ3NjExPzMsnDU1EokEwYF+yM4r1MZyC4qpuBF9BWNWVpbo7ta/JqKhqRmR4SHYuXU91Go16huanjR37R/v7Owa/QxnGo0GxWXlKC4rx5lL1+Dl4d5X6BgaDFcXp3EdP3meR/LdVFy6dmvIcdHBuDo7aRu+enu6w83V2exm3zUGiUSChXMScfriVW2suKwcZRVV8PPxEi4xAD5enng4YGbhppYW3H+YjtmzZgqY1dRkY2ON9auW4uDx/t8CnV1dOHPpGnZsXsesK5PqX3qpUqshG6FpslxuizXLF2PV0oUoLa/UFj6XVlShpraemaVzKJaWFvD19oSfjxf8fbwR6O8LZyeH0T3IITQ1NePK9ZvaZZlMhgXzZuPrX3sZGTn5kFjYaG9zdXLAj7++G7OWb0NOfpE2/us/vIONa5aZ3W9AQgghhBBCCCHCouJGQibAjceFCA8Ph6+vL5KTk1FQUMDc3tXTi+//56f48sxtfP77b5nNLI4ijsOiaHccua1f1PTUgZvF8He1hZuD1SRmpi/Cxx4pef3FaTmNQF0nDzebqXHy7EY5OxDh4mgHZ4ep3ylxsoQF+zPFjXlFJVizbD6dfAXg7OSIPc9uQX5hCc5euo5GnS6CvUolLl2/jQdpmVi9dCEiwoKZ543jONjZyWFnJ0dIkL82rtFo0NzSiuLSCmTnFaK4rBwazdADbrX1jaitb8S1WylwsLfTDvT5+XgxsyuqVCqcPHcFjzKy9O7Dw80Vi+YlwtvTA/Z2cnp9R8DzPI6evsgUNgKAi7NpXCSxeukC5BWWMjNznrpwFV9/eTfEYvrJa2yB/j5IjIvB/UcZ2lhxWQVSH2ciMS5GwMwmRlZeEXIKipnYzOgwhAQO3zlBJBLhZ9/Zhxfe/KU29iA9F6cv38KGFQsmJFdCCCGEEEIIIYQQQibDysVzkXLuS6zb822UlpYxtzW3tGDf19/E518dxrt/+QMCA/yHuBfTNDcxDsdPX9TOaqfRaHDrfhrWL58vcGYsZ0d7WFtZomtAEVZBcemUKm4sLGbfWxKxGF7uLgJlM7WEhwQyxY2FxWVQqVSQSKb3mIpEIsGLO7fh0ImzaGxqZm7r7VXi0rVbePA4A2uWL0Z4aBB8dIuMO7uYBq+1dQ2orW/QHk+GU1VTi6qaWly+cRtODg6IDAtGRFgQfL29RjWOqVQqcfzMJaRn5Q67nrWVFXy8+gsZvT09YGlJTV+fSpgZjWu3UpgZfB+mZQpe3BgXG4k79x8yBd+Xrt9CZFgw5HLTKr6fCqIjQpGZk4/svP7rvTKy8xAdHorI8BBtbLBjpqJHAdkoZ9UUiUQI9PdFoH//DNG9SiUqq2pQ19AElUqFXqUSGrUaEokUMqkElpYW8Pb0gIuzI3NthKF4nseJ0+egVKq0MY7jsHnDWkgkEr0msFKJGFZWlvjZD76BF7/xQ238/qMMnLt8A2tXLDZaboQQQgghhBBCpr7pfVaSkAmgVmuQktlX/GdtbY1Vq1bh2TVz8fGBU6isY09+300vxKZv/xHpR34nRKrjMjfcDSdTyqFUD94lrEuhwtsns/D9rTFwtBXuBHhckDMOJpegS9F/0u1mBY/t4eZfvNSu4PGwhi36WjgrigqzjCg8OABXb93XLje1tKG8sgZ+Pp7DbDW9hAYHICjAFykP0nD15l0oetkT2c0tbdh/9DQC/XywbuUSuLk6D3t/IpEIzk6OcHZyRGJ8LHp6FMgvKkFOfhHyC0uGHfBraW3DnfuPcOf+I1hbWSE8JBARocFwc3XG4ZPnUF5ZrbdNVHgItq5fBZlMNr4nYBq6fisFuQVFTMzb0x0JM6IFyohla2uD5Yvm4PTF69pYfWMz7tx/jAVzZgmY2dS1asl85BeVorWtXRs7fzUZIYF+cLCfOgX3XV3dOH3pJhOzsbbCmiXzRrX9rs0r8as//x35xeXa2G/+5wOsX05F84QQQgghhBBCCCHEvFU3tmHduvXIz89HcnIyenrYmc4uXL6KmKTF+M0vfoo3v/ma2cwCZm1lhaSEGUi+m6qN3Uh5jDVL5pjUY+A4DoF+3sjM7S9QS76Tijmz4oRLysgGvgYA4OvlBonEdF4DcxYaFACO47Sz+/UqlSgurUBocICwiZkAL093fPPVF5CS+hhXk+/oFfQ0t7Thy8MnERTgi7UrFsPNtb/g1sbGGoE21kyhkkajQWtbu7bgsaKqBoXFZVCqhh7/bGppQXJKKpJTUiG3sUF4aBAiw4IR4OczaDFVW1s7vjx8ElU1dXq3ebq7wc/HS1vQ6GBvR+MTwygsLmMKGwFAqVINsfbkEYvF2LhmOf7x+UFtTNHbi/NXbuKZzWsFzGxq4jgOG1YtRUlZBboH/L45ef4K/H29YW3d1/Dd1toKYrEI6gHXUFXV1iHcgIJTmVSqV/A4GR4+SkOxTsOKpIQ4+Pn6AACqa2qZ2+yfzGi9a9t6/Oo/30ZBcX+j/F//4a9Ys3wRHWsIIYQQQgghhIya8dr3EEIAAFUNrehWsCehv7d7CdIP/w5vPLtcb/2sokq0d3ZPVnoGs7GUIiGYLVKyl9swy00dCrx9MgstHQoIRSYRY16EGxO7U8mjVz30THDm4nYVD9WAhyERizEvPkK4hKYgDzcX2Nmy7+svj51FfUPTEFtMT2KxGPOS4vGd115EwozoQU9MF5dV4O+ffIWaATNhjoalpQVio8Lx7JZ1+NGbr+H5ZzYhYUY0bKyHnxW2q7sbD9Oz8MXhE/ifdz8ctLBx2cK5eHbLeipsHIP8whJcTb7LxGysrbFz63qT6uCbGBcDT3d2NuSrySloaW0TKKOpzcJChs1rljGx3l4ljp+9or0YYio4f+0OOrvY32rrVyzUDlqORCwW41++8zITu/swExdvpBgrRUIIIYQQQgghhBBCJh3P87hxPxMcxyEsLAy7du3C7ET9RnNdXV34wU9+jle/8V0Bshy/xfNnM8ut7R1IyykcYm3hzEmYwSwXFJeisrp2iLXNS2NTi94MdHMTYgTKZuqxsbGGj5cHE9NtcjmdicVizJudgO+8thcJM2MGHQctKinH//3jC5y+cAXd3T2D3EsfjuPgYG+HsJBALJybiN3bN+JHb76G3ds2Ii4mClaWlsPm0tHZhdRHGfj0q2P4r7/8DQePnUFGVi4Uir7rMSoqq/HeR1/qFTZyHIeVSxbg9b27sW7lEsRGhcPRwZ6KjYbR2NSMwyfPMTGRSIS5JlI07u/rjbiYKCaWnp2L4tLyIbYghrC1tcH6VUuZWGdXF85cvKZdlkjE8NYZny4rr5qM9Iyqvb0D5y9dZWL2dnZYsax/9sXSCvZx+Xu7A+ibvfJffvB15rbb9x7i8o07E5MsIYQQQgghhJApiYobCTGy8lp2dkZnuQVc7CxhL7fG/LgwvfWXz46C3GZ0F8ebilBve2aZ4wA/T/ZkXW1LN/58LAP1rcIVbi6MYgdjulRAao15F1toeB43y9nHMCsmGLajLLAgo8NxHOJj2YLRru4efHzwBJqaWwXKynTZ2tpg87oVeP2lXYPObtmrVCIjO2/c9y+RSBAWEojN61bgn771Kl7ZswPzkuLh6GA/8sYDyKRS7Nq6AUsWzKFBuzFoam7BoRNnmWI1juOwY/Ma2NuZ1ux8IpEIG1cvZV5fpUqFs5dvCJjV1BYc6IeEGewgalFpOR6kZQmUkXEVFJfhUSZ78Ux4cACiwoLGdD/Pb12DQD8vJvbr//5gShWBEkIIIYQQQgghhJDppam1HbUNLdpla2trfPz+X3Hi4Gfw9tIfK/jos/1obm7Ri5sqX29PvRmTsvKKBcpmaHHR4XoNO6/fmhqN1W7evc+cQ7W0kCFpZtQwW5CxCg8JZJYfpmchv7BEmGRMVN846Eq89tIu+Pl46d2u0WiQkpqGt977COUVoy9okkqliAgLxtYNq/DD77yGvbu3Y86suBHH3hSKXmRk5+Hg8bP4z7few8dfHsY/Pj+Ijk52pkELCxmee2YTFs5NpHHRUert7cX+I6eg6GVn6ly9dCF8vPW/14SyaukCWFpYMLFT569ArVYLlNHUFhMZhvAQdlwwPTsXOXn9DQ98vdyZ28sqzau4ked5nDp7AT0KtoH9xnWrYPHkvcbzPMor2MbSvl79Def37NiEAD9v5vZf/9c7E5QxIYQQQgghhJCpiIobCTGyMp3iRj9XWwBAQVktvvXbD5nb5DaWePeXr05Wakbj/+QxPdXS1om921fA3cWBiTe2K/DHI+l4XNw4idn1c7W3RJQvm9PpQh7dSvMtJLhaxqNJp+njokTqUDoRFs6OR6DOydf2ji589NVxmgVuCJ4ebtj3/A7s2LwWtjbWzG0qIw2miEQi+Pl4Yc3yRXjz9b34xr49WLZwLjzcXIfdzsHeDq++sBOR4SFGyWO6UCqV+Oroab2BjFVLF+hd1GEqvD3dkTgzmonl5Bchr9D0LjqZKlYvXQA7Ofvb4PzVW2htaxcoI+Po7VXi5EW2MNbCQoYNKxeO+UIAqVSCn357LxO7ee8xrt15YHCehBBCCCGEEEIIIYQIoayqnlm2trJCWHAgNq5bjbNH9+udQ3NxcYaNztiBqYuPZQvpyiprBMpkaBKJGAtmxzGxlAeP0d0z9Cxy5kCpUuHW3VQmNic+GpYWMoEympoiw4KZz6parcH+I6doFrhBeHm6Y9+evnFQ3TERAOju7sGpC1fHdd8ikQiB/r5Yt3IJvvf1fXj9pd1YPG823Fych91OrdagqKQcarWGiTs52uNrL+5EmE7xKhkaz/M4fuYS6urZa1tiI8MxJzFOmKSGYGNjjZVLFjCxhqZm3EqhMaeJwHEcNq5epldQevJ8/4ytft46xY1jKHQ2Bdk5ecjJy2diM2KiEBoSrF1ubGpGVzfb3N5vQHGjVCrFT7/Hzt54/fY9XEueGg0XCCGEEEJMHUd/2j9CiPmSCJ3AxDDXwiUDD6mGdFsztFObyIA6Wd6QvMXj39ZQosE/PvrFjTboVaqw5yfvoKOLHUT635+/giAfN5gbdwcryCQi9Kr6T5I3tnXhzZe3460Pj6C2of856OhR4W/ncjErxAXPLgyEraV0UnNdEOWBrPIW7XJzD3Akj8fz0eb3E6a2k8eJfPb45ufpigBv83sPjUjI7pFPOsBKpVI8t3UdPjl4AuVVtdqbW9s78PGBE9i3eyvkOp1wBcVrRl5nyG0Nfb455v/FRIQiMzsP2QO6FVrKZNrn1lg4joO7mzPc3ZyxZMFsNLe0Ije/CNl5hSirrNZ28w3w9cbOrethbW0F8/2NMPl4nseJs5dQU8deoBIdEYp5SfFjv0Nu8npqLF88H1l5Rejs6h9gOX3xBgL9/SCVTu73kEE4A9+vkzQroKWVFTatWY7PDp3QxhS9Spw4fw17dmwaR0dgAT+nA37fXUq+jZa2Tua9u3rpQtjZO4zrrl/auRW/+Z9/MBdA/fp//oGlC+eNO10tjQHHcd7Q4nND/h1g4Gst1M8Fc83brBnwO0dAgr7UNDPs5Jq2z/f4P5sGz16sMeD7y5B/Oxm670F/50zX9w8hhBBCCDF3ZdXsuWM/H09wHAeVSoVvfO+Her/7f//vP4dMZl6FabqztFXVNUCpUkEqMa3LLBbOScC5q7eg0fQ95wpFL64lp2DtisUCZzZ+d+49RHtHJxNbPCdOmGSmMGcnR8yZNRN37j/SxlRqNb44dBIv7toKXxOarc4UcByHmKhwhIUEIvluKpLvpkKl6j9P0NzSapR9eHm6w8vTHcsXz0NjUzNy84uQk1+EiqqaEc+pBAf6YcfmdbCysjQ4l+nkzr2HyMjOY2Luri7YtHa5Sc58mTAzGg/TM1FZ3X8dw/Vb9xATGQZHB3sBM5ua5HJbrFu5BEdOndfGOjo7cfbSdWzbuBp+OjM31jc0oau7G9ZWVpOd6ph1d3fj1NkLTMzG2hprVy1nYroFm7bWVnC0Z2ea3bt7K37zx7+ivLJ/hsff/PGvWLJgtpGzJoQQQgghhBAyFdHMjYQYkVqtQWVdCxPzdbXFz976Cvcz2RmjXt6yGM+tM8LF7AIQiTj4OLNFXeVV9XCws8X3X30Gvp76M6ilFjTgt/sfTeosjtVNXTiXqt9V8lYlj+wG87p4UMPz+CxTA6XONZjbV883yZPpU4VMJsWe7Rvg6ebCxJta2vDxgRPo7OoSKDPTpzvTn6WlxRBrGo+jgz3mJsVj354d+OdvvYqdW9Zhz47N2Pvc9ieFjWQsUlIfIy0rl4m5ujhhy7oVJn/csbKyxOplC5lYS2sbbty+L1BGU19ocIBeF/OC4jI8Ss8WKCPDlFfWIOVhBhML8PVGwozIcd+nTCbFj7+9j4ldSb6vtx9CCCGEEEIIIYRMMxxn2J9QeN7AP42Z/gn0mA1+vYy/7/LqBmbZz9cbAPDr3/0RN2/dZW7bvWMb9r30vOGPY5L5+rCFXWq1BlV1TYBIPMKfZPx/43iNHO1sERsZyuR6+sJVVNeyBajmoqm5BUdOnmdioYG+8HRzRl+DGN0/w/A8P+4/YQ32XIz2r9+a5YswMzqCifUqlfjswHFU19RNQN6GzL9gyGPmwfOacf8NJJPJsGzRPGzbsJqJ687sZgzOTo6YP2cWXnnhWXz/G/uwYfUyBAf6QSQWgdf535zEmXh+x2ZYWlqYxHvUkOfbkM/lWP+KS8tx/upN7fMI9L2Wu7ath0wmhaHvu4n4E4k4bFi1lBmzVaqUOHvp+gS8kqZucp7zGdHhCAsOYPb8ODMbeQXF8HJ3gUTMNud/mJZl/Ic6Ac5dvKJ33cu61Stgbc3Otv3gcSaz7OvlCg7s71uZVIIfv/k1Zr1L12/jdkoqDP8dbeBvaYP3bY5/Av6bSaOmv8n8E/S1NvDPXN/jgh2PDNy3kL9fBH/eBfgT9PciIYQQYp6ouJEQI6ptaoNKrWFiOflF+NPHZ5hYeIAn3vrJi5OZmtH5urLFjRU1fYNjchtrfHffdsSGB+pt096txN/O5eLtk1lIL2nSdg81tvKGDnx+tQC/P/gYZQ2dg67zeZYG3Urz+SF/tYxHUQsbWzI7FqEBXoOuT4zH0tICL+7YCDcXJyZe39iMTw+eQk+PYogtp7eenl5m2WKSuzHb2FgjKiIUocEBJl+IZ4ra2ztw/upNJmYhk2HX1vVm01l7RnQ4/H3ZY2RyygM0NDYJlNHUt2bZQr0ZbW/cSRUom/FTqVQ4fu4qBg76SyQSbF6z1ODjyb5dm+HlwTaB+PLYOYPukxBCCCGEEEIIIYSQycbzPMqq2KInP28vXLuRjN/8/k9MPDDAH//31h/M8ly9laUl3FydmVhZZY1A2QxvzZJ5TL2zSqXCJ/uPQKPRDL2RCeJ5Hp8dOKbXRHPNkjkCZTT1cRyHLetXIio8hIn3KBT45KujqKufvObF5kat8/mysJjYMTS53BaJ8bF4YedW/PDbr2HH5nWYERWBkEB/7NyyHmtXLIFIRJeBjUVbWzsOHDujd6zcvnE1nBwdhElqlLw83ZEUH8vEcguKkJtfJFBGUxvHcdi4epleEfOJc5egVCoRFuzHxK/fSoHQBcYjKSwqxqM0tglreGgIoqPYgveW1jY8ymAb2kaHBQx6n688/ww83dmx0N/88X8NT5YQQgghhBBCyJRHZ7UIMaIehZJZVvf24Bv//j4Tk0kl+Pz334KNteVkpmZ0TnL2hN3AQiYrSwu88fxGvLR9FawGma0tp6IF757Nwb99/gDnHlSgvbtXb52xUqo0SMmrxx+OpOH3B9NwK6cOqmGKJ5t7gP3ZPDQmfjIRAMrbeJzIZ/N0cbTDlhU0iDdZrK2t8NKzm+DsaM/Eq+sa8PmR0yZ/UloIil72c21paR4FcaRPXmEJ1DrF+ts2rIKLs9MQW5gejuvrWCoS9V/NoVarcfriNfrMThBLSwtEhbEXP6jUaoGyGb8bdx6gXqcIdtmCJDjpfAeMh6WlBV59bisTO3L6Mr0nCSGEEEIIIYQQQohZaWrtQGc3W3xma2OFPa98gykQkUgk+OLDd2FvbzfZKRqNnw/bRK+8qlagTIYX4OuF5QtmM7GSsgpcunZLoIzG51bKA2TnFTKxeQkxiAoNECahaUIkEuGZTWv0ZiXr6u7Bx/uPoLGpWZjETJxCoTMeOgEzNw7F0tICMZFh2L5pDV7YuRVREaEjb0QYPM/j0IlzerPWLV0wB2Eh+s28TdHyRfNga8POsHfm0jX09hp+DQ7RZ2cnx5rlC5lYe0cnbt65j8WzZzLx8spqlJRVTGZ6Y9Lb24sTp9kGrBYyGTasXaXXkOLW3VTm951UKsGcmZGD3q+lpQV+9B129sYzl26gsto0fz8RQgghhBBCCDEdVNxIiBH1qvov4Od5HucuXERdUxuzzn9+fzfiIvwnOzWjk4jZw4dSpWKWOY7DnLhI/Ou39yBmiI5dTR0KnEgpw79+kop3TmXhREoZHhc3oqldMeJF/l0KFXIrWnDhYQXeP5+Ln396Hx9fzkdJbceg67s52cPPk+0Odr+Gx+Fc3qQLCmo7ebyTqoFSp6nqC1uWwUImFSapacrWxhovPbsJDnZyJl5WWYO8wlKBsjJdujNaTuZgHjFcTV09sxwZFoyIsGCBshk/N1dnzEuKZ2JFJeXIzMkXKKOpTa1WI0PnuY0MDRIom/GprW/EjbsPmJiXhxvmJc4cYouxe2bDCma5tKIaD9NzjHb/hBBCCCGEEEIIIYRMtNb2TmZZIpHgn3/6C1RWVTPx3/zip5iTNGsyUzM6T3c3ZrmltV2gTEa2afViuLk4MrET5y6juqZuiC1MS2NTCw4dP8vEHOxs8cz6pcIkNM2IxWLs3LoegX4+TLyjswsf7z+K1jbTfe8LRXeGUctBGj8T01VTW4/SikomFhoUgCU6heKmzNLSAquXscV2La1tuHH7vkAZTX1xsVHw8fJgYpXVtYgOC4SjPXstyfVb9yYztTG5fPUGWlrZ69lWrVgKO53rYdRqNW7cYd9PSTPCYW019PHutRefhYNOY4ujpy8amDEhhBBCCCGEkKmOihsJMaKBsxM9evQIpWXlzO0bl8Tj28+vnuy0JoRUr7hx8JmZHOxs8fU9m/DS9lVDzlap1vDILm/BuQcV+Nu5XPzis1T89KN7eOt4Bt49m40PLuTiHxfz8N7ZHLxzKgv/9nkqfvSPFPzlZBaO3S3Dw6JGdPSoBr1vkUiEFfPj8dOv78TXdq7RKwi8WsbjRIFpFjjWd/F4O1WDDnZCUCydHYtQf6/BNyITyt5Ojpee3QS5Ldv9sLyqRqCMTBPP83qDeRYymrnRnNTUNTDL3p7uAmViuCXzk2CvMwhz9tINvQJcYric/CK97razZkYLlM3YaTQaHD93hek8KhKJsHnNUohExvtnU2xkKIID2ItDDp2+ZLT7J4QQQgghhBBCCCFkouk2Pc3KzNSb/WflsiX44fe/PZlpTQgLC3Z8Q/exmxKZVIoXn1mHgRMuqVQq/PX9T/UKGExNR2cX/vr+J3rjS3u2roa11eBjzMT4JBIJnntmI3y9PZl4a1s7zl2+IVBWpkt3rInGQ81LlU7ht62NDbZvXKM3a52pi40K1ytKvnXvAeobGgXKaGpTqVRoaGRns/Xx8oBIJMIindkbUx9noL2DbQhhCioqq3D3Ptvs1d/XB7Pi9Zu9pmXm6BW3L0qKHfb+ra2tsGnNUiZ25BQVNxJCCCGETCQRx9Pfkz9CiPmi4kZCjEj85ML32tpapKSkMLd5uTri/V+9ZnYnQoei0SkGFA9z0f/TWRx/809fw56tq+Dr5Tbkuk919KiQV9WG9JJmPChsRGpBA9JKmpBd3oKGtpELUuzlNtiwbA5+/f292L5mIWRSCZwd5Nj3zEq9AoXzxTwO5PB6j0lIle08/pSiQXMPGw8L8MLWVXOFSYoAAJwc7TEzKpyJNTQ1D7H29KRSqZjiIIA6lZoTnudRq1Pc6OHmOsTapk8mk2HtikVMrKOzE1eT7wqU0dR1/1EGs+zv4wU3V2eBshm7u6lpqKxmB7IXzI6Dh5uLUffDcRy2rVvOxI6cvmzUfRBCCCGEEEIIIYQQMpEGNj1taGjA1WvXmNtdXVzw8d/eNmrTMKFIJRJm2ZSLGwEg2N8HyxckMrHG5hb85b2PTLLAAgC6e3rwzt8/QXVtPROflxCD6PAggbKavmQyGfbs2AwvD3ZMPye/EGr14A2PpytFby+zTOOh5qWmjj3mBPh5w8oMi6k5jsP6VUsgFou1MbVajVPnr5pkk29zl5mTr1eIPysuBgAwf1YsxAMaxatUKhw+wTZ/EJpKpcKxk2eY94ZEIsbmDWv1rmdTKHpx5NR5Jhbo4wG/UVzztW3DKmb52q17ekWhhBBCCCGEEELIQOY/mkCICZFIxFAoFLhw4QJT2MNxHD75f9+Ai6N8mK3Ni1LFFi5JpZIh1uwnk0kxf1YMfvz15/HDN57DnPgoSCTiEbcbi/AgH7y2ax1+/f29WL90NhzsbJnbY8MCsHfrcuiWmF4v5/G3Rxq0KYQ9ucvzPO5Xa/Df9zRoZ8dC4O/lhjd2r9MbRCWTz9nRnllubG4VKBPTNNiMeJYWNJhnLpqaW9CrZKeMNXZx12SLCA1GaFAAE7ub+hg1OhdKkPFraGxGcVkFE0t8MpBnDppbWnH5Jlvw6uLkiCXzEofYwjDb17PFjTkFJcjOL56QfRFCCCGEEEIIIYQQYnRPhtOUSiUuXLigV/D08d/ehqenhwCJGZ/uhf7mUCeyadUivWaz1bX1+PNfPzC5GRw7OjvxP//3IUrLK5m4m4sjnlm/VJikCCwtLfDc9o1MTKPhTe79IzTdMVFLC5q50ZzoFjeac7NXVxdnzE9KYGIl5RVIz8oVKKOpS7fZa0igPxwd+q4fkdtaY1ZsBHP73dRHSMvMmbT8RnLz1l29WT2XLloAZ2cnvXWPnr6gV5C4eM6MUe1n9dIFsLa20i6r1WocP3dlHBkTQgghhBBCCJkuqEKGECOSikW4fv062tvbmfjPXtuCpUmRAmU1MboUbFdS6RiKFDmOQ4CPBwJ8PPDM2iXILSpDWVUtyiprUVZVh+5BCqMGI5GI4e3uAj8vN/h5uiIkwBtuzg4jbpcYG4pepQqfn7iKgeOP6fVA4S0NdkZwmOXBTfosm20KHl9ma5BWp3+br4cLvrlnAw2ImAgXJ0dmuam5FRqNZkp0IDaGHkWvXsyC3rtmo0Zn1kZbG2vY2toIlI1xPO1Y+s77FVA96aqdnpWL13/wr0iYEa093g/8r53cFovmJiJhZrRgeZuT1MfsQJ61lSUiw8yjozbP8zhx/qpex/XNa5ZCMkENBWbHx8DLwxVVNf0D54fPXMXPQgMnZH+EEEIIIYQQQgghhBjT0+alN2/eREtLC3PbP735TaxdvUKArCaGUsmeN5SNouGr0GRSKb798rP443ufo66hvyihpq4ef3z77/jGK3vg5ekuYIZ96hua8L8ffKZXYORoL8eb+56FtRnOoDaVyOW2sLG2QmdXtzbW0NgMZ51x0ulMd/Y2avZqPnieR63OmKi5N3tdPD8J6dm5TBHy+Ss3ERYcSLOKGklNbT0qqmqYWGJcLLO8edVCpGUXMNdMfHHoBEIC/ZliPyHU1dXjxq07TMzTwx3z587WWzevsBjXktnGsMH+XkiMDR/VvqytrbBu+SIcOtk/8+ORUxfxyvPbx5E5IYQQQgghhJDpwPTPvBNiRi7fTEFBQQETS4wJxs/f2CpMQhOosrGLWXZ1chjX/dhYWyIhJgwJMWEA+k4iNza3oqyqDs2t7VAqVVCq1NDwGsjEIkilEthYW8LHwxVebk4Qi8c38+P8hEhYWEjx0eFLUA+YZbNLCXyYzuNhLY9dkSLYWUx8gSPP80it4fFVDo8upf7twX6e+MZz62BFJ5xNhr3OjKBqjQbNLW1wHufnYKpR9LLFjVKpdNyfVTL5dGczNOcupQM5Othj8bwkXL5xGwBgYWEBV5e+DpT8k1bb/ICW280trTh14SoC/Lzh5Ogw6fmaE6VSiUcZbMfR+NjICSsMNLZHGTkoKmVnnZwdHws/H88J26dIJMLWtcvw1w+/0saOnL2Kn725b8L2SQghhBBCCCGEEEKIscikEuTn5yMnhz0vOCt+Jv7jVz8TKKuJ0atkxzzM5byn3NYGb76yC39673M0tfQXujQ2t+B3//1/WL96GVYtXSDI+I1Go8G1Wyk4duoCepXs4Kjcxhpv7nsWTg52k54X0efs5MAUNzY2twiXjAlS6DR8pWav5qO5pVXv+OPuat7FjVKpFOtWLMEXh09oY9l5Bfj3//oLZs2MAQC9hq/2dnLExUbSWOgo3X+UzizLbW0QFsI2LnW0l+OZ9Uvx2ZH+or7WtnYcOHYGe58TrrBPo9Hg+OmzzGzbIpEIm9ev0WvirVD04tOvjjIxqVSCF7atgkg0+muotm1cxRQ3Xrh+G23tHbCT2w6zFSGEEEIIIYSQ6co8zrwTYgaqahvww9++zcRkMhl+8vVd2u6lU0lZfQez7OtlnOIXjuPg4uQAl8GKxDQq/ZgBZkWHwNrSAh8evoiOrh7mtsd1QH6zBot8OCz05eBoafwiR7WGR2YDcLVMg7ymIXKMCcELm5dCJpUaff9kfBSKXhw6dVEvPtoZR6eDnh7dLqU0kGdOauunVpfSgebPjkdaVg6C/H0R5O+LotLyYddXq9XILyrFnFkOk5OgmcrKLUR3D/s9OstMZrxs7+jEuSvJTMzeTo4Vi+ZM+L6f2bCCKW58kJ6LkvIqBPh6Tfi+CSGEEEIIIYQQQggxRFNLC65fv87EbG1s8OVH70Emm1pjAroze8ltrQXKZOycHOzwg9efx18++Aq1Df2DkSq1GsfPXMTjjGy8uGsbvDzcJi2n+oYmfPLVERQUlQ6Srxzf2fcs3J80JiTCc3FyQllFtXa5obF5mLWnH5q50XzpNnu1sbaGra2NQNkYT3hoEMJDgpBbUASgr6iupbUNUqlEW9CoKy0rB998Zc+U+/42NoVCgbTMXCY2a2aMXmEgAMxLiMHDzHxk5RVrY3dTHyEiLAhzZsVNdKqDSrn/ABWV1Uxs/pwkeHp6MDGNRoMvDp/QO95vXjkfbs6OAK/BaG1YtQRSqRTKJ4XEvb1KnL54Hbu3rR/noyCEEEIIIUPhnvxNd/QcEGLe9P+FTQgZM57n8fV/+S80t7Yz8aVLl6IXU+8Ednt3L1o62S6Efl6TN+hlTJHBvvjXb+5GQnSw3m1dSuBcMY9fXNfgvYdqZDfw0AyY1Wu82hQ8zhZp8G83NXjv0eCFjbbWlnj12dV45ZlVVNhoQhSKXnx66CRKK9iTvna2NnCjgVYtvYE8mnXUrNToXKjh4T41Zm4E+jpqv7F3N9avWgrpKI+tZRVVE5yV+bv/KINZDg7wM5sOrxev39Y7Zm1ctXhSuisvmhMPZ53n6cjZaxO+X0IIIYQQQgghhBBCDKHRaPDjX/0Bvb3sWOFPf/R9hAQHCZTVxNE9R+zr5S5QJuPTV+D43KB5l5ZX4nd//l+cPn8FXQNm55sIPT0KXLyajN/+8Z1BCxvdXZ3wg9eeo8JGE9Le3qH3/m9pbRti7elJr+ErjYmaDb3xUDeXIYv/zM26lUsglUhhJ7fFvMR4xEaGD/vYWlrbkF9UMnkJmqn0rDxmtk+O45AwY/BmrxzH4fktq2Clc0z4ZP9RpGXmDLrNRGpubsGlq2xTCmcnRyxZNJ+J8TyPQyfOIiX1MRMP9vfC0rnxY96vvZ0cq5bMY2JHTl8a8/0QQgghhBBCCJkepubMjZyBNZsGnbAyYN+82oD9GogzcGZBoU7yGZq3IYVq4v6iiE8OnsbJS7eYm0NCQhAcHKw3w+FUUFbfySzLZFK4u7qM/NkbQwevwbefmM+I3MYKr+5YjfioQuw/dV1vFkceQFo9kFavgYsVEObEwc8O8LPn4GkLSEXDv/9beniUtwFlbTzK2njkNALqYd56CdHB2LluEeQ2VsJ9tsyZoQWoQzznPT0KfHroJCqqapm4tZUlnn9mA2RCz05oyMymhn62xOxjV+gUClnIZOj7JA3GgPe4RsDvTUMZ+ltl3Psd/vnu7OxCWzv7veXu6jzq7YdlaG24kY6H0iddR9evXooVS+aDf3LMePrfvMJiXL5xR7t+WWU1eECgAU0D92nI5qP8zm5obEJ5VQ2zs8T4WAPf44Y3EhiN9o5OpGcXYGDuM6LDEBocOL47HOPvUolUjM1rl+IfXxzVxo6cvYbvv/Hi2PdthOYLgjD0YyXU46afZ8QsGPhvL0JGw4DjsMGHUkO+A0QGng6Vjb+LPyfS/73AWdIXCyGEEEIIMS/vfnwAV26mMLGwsDDMiI0VKKOJ09vbi2qd2b38vD2GWNt0yW1t8M9v7MHJSzdx8cY97flwoG8Wx5Pnr+DclZtIjI/Fkvmz4efjZbR9V1XX4tqtFKQ8eAyFonfQdRbPicPWNYthKfRYG9Fqa2vHR18eQWNzCxO3t5MLk5CJ0m2eaEEz35mNmjr22O7hNnWavTrY2+H5HZvg7emOQyfOaWdxHE5ZRTWiI8ImITvzxPM87j9KZ2LhIUGwG+aY6GgvxzPrl+LTw+e0MY1Gg79/8hVe37sbMZGT93yfPHMeSiV7PcnmDWuZZsA8z+PY6Qu4MmCcHABkUgle2LYKohGuixrKtg2rcPpif2Hl6YvX0d3dAysry3HdHyGEEEIIIYSQqWtqFjcSMokqq+vw3V/+iYlZWVlh0aJFfbc3dkKp0kAqmToTpZbUsjNU+nq6QiQy/8eXEBWMUH8vHDhzE6mZBYOu09ANNFTyuFUJADzEHOBlCzhbAVIxB6mor3BRqQF6lDwq2oG2wcfp9MhtrLBz/SIkROnPIkmE1d3dg08OnkRVTR0Tt7G2wks7N7PFX0R/5kYajDYbtfVsl1KpRAJnJ0eBsplYQw3ASyRiprixvaMTLa1tcHSwn6zUzEpFVS1CgvzR0NiE2vpGODk4ICw4QOi0RuVBWhY0mv7iH6lUgjVLF0xqDtvXr2CKG5PvPUZNXQM83FwmNQ9CCCGEEEIIIYQQQkajuLQCP/p3dlzU1tYWCxcu1JvhbSqoqKphCgE5joOvp3nN3PiUVCrBtrVLERcVho8PnUZtfRNzu1KpxO2UB7id8gABvt5YMDcRQQF+cHd1HtM4sEajQUNjM4pKynAr5QEKivVnaXzK2dEOL2xfi/Agv3E/LmJ8rW3t+OiLw2hqaWXijg52WL5orkBZmR61Wq1XLEQzN5oPveJG96lT3AgAgf6+AIDoiFC4ufRdyzCw4Wt1bT2KSsu060/F73Bjqqyq0ZvtMzEuZsTt5sZHo6qmAZdvpWpjKpUK//ePz/Hy888gMW7iG0NUVlWjsLiEiSXNioO/n692WaPRYP+RU7hx+x6znkgkwiu71sPNefzXC2xeuxxv/NMvtWOynV3duHDtNjavXTbu+ySEEEIIIYQQMjVRcSMhBuB5Hm/85HdobWNnuVqyZAksLfu6TKnUPB4VNSIpbGqcDNXwPO7lsyft/LzMcxBvMHIbK7yyYxXWLk7AjXuZuJuWB0Wvcsj11TxQ3t73N96Zpvy93LA4KRoJ0SGQSemwbGq6urrxycGTep15bW2ssXfnZri6OAmUmenq0em8SwN55kN3UMZtjBctTAXOTo6wtrJEV3f/LL7lldVU3DiEnPwiFBT1XZwiFong5eEKsdjAmbUngVqtxv1HGUwsNjIMNjbWk5rHioVzILe1QXtH36zYPM/j6Nmr+PpLOyY1D0IIIYQQQgghhEwSnjdsFuqh7tMsCZS3kM8XZ96zdms0Grz6/V+is6ubiS9duhQWFhYoq6gCz/PgzPxxDlRWUc0se7g5Q2YxijEPQ95nvGbkdQzYd6CvJ/7lW3tx8lIyLt5kZ3F8qqS8EiXllQAACwsZfL084efjBV8fL9jLbSGRSiARS6BWq6BUqtDe2YmyiiqUVVShvLIaPT0KvfvUZczZGgd7DObBwLwn4HG3tLbhwy8Oo6W1jYk7Ozlg7+7tkNvaPNmvIe9xQ7Yd/6YADDoO82DHPnp6FOB1ErKQyQZ9Pxp6XDTsPW5677NR7njC7rmzswttbWxDbw9X5/7jryGvl6C/M/RDM6LDB121pKyCKW6sqauHQtEDi9F8x01DTS2tCPD1RnllNdQaDRzs5QgO9MNI71OOA7avW4yunh7ceZCpjWs0Gvzjs4MoK6/CxrXLIRswg6Kx3Ut9yCzLbW2xctkS7XJTcws+PXAMOXmFernv3b4asWGB7Pt6jO9xV2dHLJ6XiKvJ/bN+Hzl9AZvXLh3T/Yxn3zobG7Ctgcz1OGrob1LBmOvvQkO+ewzdtSHXBBn6bz9DtjfXf3ca8IIZ+m9tg45H5vp8T1cCfWcO9t1htt8nhBBChEBVNIQY4MOvTuH05VtM7IXtazFr9jzkldZoYzcya6ZMcWNORQsa2nqYWFx0qEDZTBwvN2fs2rAYW1bORUpaHq7fy0B1fbPR7l8qEWNWTCgWJ0bD39vNaPdLjKuzswsfHziB2vpGJi63tcHenZvhYkCHuqlM93RGd0/PoOsR01OjU8Q7HWeP4zgOfj5eyMkv0sbKKqowIzpCwKxM18DOthKJBNER5vGbILegWFtQ+FRS/MR3R9VlaWmBDSsX4cujZ7WxI2euUHEjIYQQQgghhBBCCDE5f/3Hfly9xc7oExUVBV/fvpl/2to7UFZRBX9fbyHSmxAZ2bnMsp+3p0CZGFffLI5LMCs2HBdu3MPDzDztjEq6FIpeFBSXDjsD42hxHIcZEcFYuSgJwf5T530yVTS3tOLDLw6jVafoy8XZEXt3bYNcbitQZqapR6FfxEsNX81DbT3b7FUikcDZyUGYZATi7ekOsVgEtbrv2M/zPCqqahAc6C9wZqbpzv1HqKqpA6/hoejtRdCM6FEXLXMch+e3rIJGo0HKo2xtnOd5XLyWjPTsXLy0a5t2tk1j6urqQkZWNhObnRgPCwsL8DyP5LupOHzinN7xjOM47NmyAokzBi+OHattG1YyxY3Hz12FUqmEdAKLOgkhhBBCCCGEmB8qbiRknMqravH9X/2ZiXm4OeO//+37KKlpZoobi2rbUdHQCR8Xm8lO0+huZNYwy17uLgj28xIom4lnaSHD4qQYLEqMRkllLQrLalBWXY/yqnrUNbWO+n4sZFL4errAz9MVfl6uiArxg42V5QRmTgzV0dmFj786jrqGJiZuJ7fF3p2bp90Ax1h4uLPF3BVVtVOuW/NUpTtzo4fb1CjMHyv94sbqYdaevrq7e9DSyl7k4OluHu+Zew/ZWRt9vT37chegY9i29SuY4sYrt+6jqbkVTo40WyghhBBCCCGEEEIIMQ2FJeX4yW/YcVE/b0+sW7MSXT1Kbez67Xt4cYoUN9Y1NCIrt4CJRYQECJPMBPHz9sCruzehtb0Dt+6n48a9x3rnfI3BztYaCxJnYEHSDDg52Bn9/onhGpua8dGXR9DW3sHEXV2csHfXNtjamv91DsamUqmFToGMk+54qLurM0QiQ2atMj9SqRSe7m6oqOq//qe8koobB6NWq1H3pBk2J+JgaWmB2KiwMd2HWCzCi9vXwsrSAtfuPGJuq61rwB/e/jtWLJmPTWuWG7Xg7+HjdOZYJRaLkRA3A03NLfjswDFk68zW+DTXfTvWIt6ITe63rluB7/7Lf2iXm1vacPXWPaxaMt9o+yCEEEIIme440ByrAD0HhJg7Km4kZBx4nsfrP/p/aGtnZ/x593c/gZOjPezt5LC3tUJrR7f2thuZNXhuSfBkp2pUTe09yChlZy9cPHvGtChY4jgOgT4eCPTx0Ma6exQor25ARW0Dunt6oVSpoVKpIBKJIJWIIZFI4OIoh5+nK1ydHSCaBs/TVNHe0YmP9h9HQxP7fre3k2Pvzs1UcDICHy8PZlmh6EVDYzNcXZwEyoiMhkqlQkMTW8yrW6g6XfjqdN+ua2hEd3cPrKgonVGtM9OnRCI2ixlt6xuaUFxWwcSS4mIEygZYt3whLC0t0NPT1xVVpVLj5MUbeOnZjYLlRAghhBBCCCGEEELIUxqNBq9+7xfo6u5h4n//86+gghQnrvTPxHP/QRqe2bgG1tZWk52m0d24zc5SaWNthfjYCIGymVj2clusWzYPqxfPRnpuIe4+zEJJeTVadQrdxkJuYw1/Hw/MjotCXFQoJBKxETMmxtTQ2ISPvjyC9g722gd3Vxe8tGsrbGysBcrMtDnYy8FxHHie18aqa+vh5zN1G0NPFTV17PjW9G326skUN5ZVVAmYjelqaGyGSs0WM4/nPSMScXh2wzI4OdjhxIVk5j55nsfFq8l48CgDi+YnYX5SgsGz5Wo0GtxLfcjE3N3dcfzsZaSkPoait1dvG3u5DfY+swbhQcadRdLX2xOzE2KR8iBdGzt86hIVNxJCCCGEEEIIYVBxIyHj8P7+kzh37Q4Te2nHemxatQhAXyerBXFhOH3zsfb2e/n12DTbD7ZWxuuyNdmuZ9RgwLl5WFrIkDQzUriEBGZlaYGwQG+EBU6NDrSkT1t7Bz7afxyNzS1M3MFejr07t8CROsqOyE5uC3s7OVrb+rv7lldWU3Gjiaurb4RG03+Q5zgO7q7OAmYkHE93V0jEYmZQqbyyGmEhgQJmZXp0B3/dXJwhFpv+BSr3HqUzyzbWVogKF64Bha2NNdYsmY9j565oY0fOXKHiRkIIIYQQQgghhBBiEt7+4Atcv5PKxN546VmsXDwXrR1dOH3tPtQaDQBAqVLh9v2HWLHYvC9W71UqcTuFLQiYlzgDMiPOpmSKxGIR4qJCERfVN1tTa3snyiprUFZZi7KqWtTUNULR+7ThqxpisQgyqQRSqRQerk7w83aHn5cH/Lzd4WBnOy0a5Jq7+oa+wsaOTraw0cPNFS/t2jolCpUnikwmg7urCzNWUlFVQ8WNZkB35kYPNxeBMhGWn7cXbqH/u66iqgYajWbazWI5kuraOmbZwV4+7oa4HMdh5cJERIUG4JPD51BWWcvc3tTSimOnL+LUuSuInxmNxfNnI8jfd1zfpwWFxWhpbYNGo0FNbT1Kyyvh7lkDW9vBiybnxkfimbWLYT1BzX63rV/FFDcePX0Jb/+/fzGLsWVCCCGEEEIIIZODihsJGaOyylr802/eZmJe7q748y+/x8QWxIfibHIaNE+qAXtVGhxILsa+lWGTlapRVTR04nJ6NRObHRcJSwuZQBkRYnytbe346KvjaGpuZeKODnZ4edcW2NvJBcrM/Ph4uTPFjRVVNUiYGS1gRmQkugN5Tg72kMmm5zFeIpHA29MdpQM6lFJxoz7dmRs9zWCmT4WiF48zc5lYwowoSCTC/rNo2/oVTHHjuWt30NHZBVvqiE0IIYQQQgghhBBCBJRfVIqf/vZ/mFiArxf+8xc/AADY21ojLioIqRkF2tuv30rBsoVzzbo4IvVRBrq6u5nYojkJAmUjHHu5DWIjghEbIVxzODJx6uob8dGXR9DZ1cXEPd3d8NKureMu3plOfLw8mOLG8srqYdYmpkClUqGhsZmJeZjB+NZE8PX2ZJZ7lUrU1NbDy9NdoIxMk+4Yuqe7m8H36eXugn9+bTcu3ryP01fu6M0MqVKrce9BGu49SIOrixMC/Hzg5+MFfx8v+Hh7wtLCYsj7bm1rR1lFFT76bD+yc/PQ3NIGpVIJa2tr2NjY6K1vL7fB85tXICZ8YsfBt21YiZ/+5k/a5dr6RtxJTcOC2fETul9CCCGEEEIIIeaDihsJGQOe5/G1H/8O7R3sCf73/vOnerO5OchtkBAVgPuZxdpYakED4oOcERdkXjNhqdUafHqlgJnRSyTisGROnHBJEWJkLa1t+Oir42huaWPiTo72eHnXFtjJB+9gRwbn4+WBzJz+ixkqqmsEzIaMhu4sfNN1IO8pPx8vprixbMD/J330B/NM/z2TlpULhaJXu8xxHBLjYgTMqM/GVYshkYihUvUNXvb0KHD2yi3s2LhS4MwIIYQQQgghhBBCyHSlVqvxyvd+ie7uHib+/p//HXLb/ovjFydGM8WN9Q1NuHknFYvnJ01arsbU06PAqfNXmFhUWBBcnR0FyogQ46uprcfH+4/qFfF6e7rjhWe3UGHjKPl4eeD+o/6ZyCqqqsHzPM1aasLq6huheTLb8FNuLuZ1/Y6x2NhYw9nRAY3NLdpYWUUVFTfqqK5hZ2401kyfYrEIa5bMRmxEEL44fhFFZYMXR9c3NKG+oQn3HqQB6BvbdHVxgo21FaQSKcRiEZQqFVRKFZpb29Da1o6enh6kp6cz9+Pm5sYcmziOw9z4SGxfs2jCZmscKCw4ADERIcgYcA3J4VMXqbiREEIIIYQQQoiW+bZLJEQA731+HBdv3mdiL+/ciPXL5w+6/vYVibC2ZGe92n+jCB3dygnLcSKcf1SJisZOJrZqYRI8XJ0EyogQ41Kr1fjkwEm9wkYXJ0fs272VChvHwcfLg1mub2hGT49CoGzIaOgVNxppYMZc+fl4McuV1bVQqVQCZWN6ent7za6zLc/zuPcwg4mFhwSaxKy8To72WDY/kYkdOXNliLUJIYQQQgghhBBCCJl4b/39cyTfe8TEvrlvN5YtnM3Egv084eXGjhkeOXUOjU3s+UNzcfjkOTQNKPQAgMVzZwmTDCEToLqmDh/tP6JX2Ojj5YEXd9KMjWOhO/Nde0cn2to7BMqGjIZu404nRwdYWMiGWHvq0x0PLa+ihsUD8Tw/ITM3DuTl7oIffG0X/vn13ZgTFwmJRDxiTnX1jSgurUBeYTGy8wpRUFSKkvJKtLa1AwDq69lxf7FYDCenvt9qcltrrF0yG7/+wT68sHXVpBQ2PrVtA9vU9cipS+B5foi1CSGEEEIIIYRMN1TcSMgolZRX44f/8Q4T8/ZwxZ9+8d0ht7G3tcazq9kBvvZuJQ4kFw+xhempbOzE2dQKJubp5ox1y+YIlBEhxldYUs50JHwqLiYctjbWk5/QFODp7gqxuP9nBs/zqKyuFTAjMhye51Fb18jEPNxMu1BtoukW6KrUalTX1g+x9vRTW9/IDDZxHAd3V9PubFtWUYW6BvZ9nmQCszY+tW3dMmb55MWbzCyThBBCCCGEEEIIIYRMlrzCUvzsd28zsSB/H/zuX7+nty7Hcdi+mm0Eq1D04tOvjprdBes5eYW4eYdtdBsa5IeYiBCBMiLEuOobmvDR/iN6M7L6+XjhxZ1bYWlpIVBm5snJ0QFWlmxhUEXV4LOvEdNAzV5Zfj5sgW5ZRZXZfXdPpOaWVih62bG6iXjPcByHQF9PvPTMWvz2n1/D1jWL4OxoN677UqvVaGhgCzJdXFwQHuyHV3aux29+8Ao2rZgHR/vJb/66Xae4saS8Eo8yciY9D0IIIYSQqYgDwHH0x434TBFCTBkVNxIyCi2t7fjaj3+Hjk62e+Hf/vNf4DDCCZ+k6CDEhvoysdSCBlxNN/2T2u3dvXj/fC7Umv6TlyIRhxe3r4ZUIhEwM0KMy8FODo7T/1l78fod/OOLo6itbxxkKzIciUSi17Wwgjo9mqyW1rZJGZgxJ1ZWlnBzYYv1Uh9lDLH29FOjU+jp4uwIqVQqUDajk/IwnVl2dnJAUIDvEGtPvi1rljLfRe0dnbh4I0XAjAghhBBCCCGEEELIdKRWq7Hvez9HT4+Cib//538fsiFkZLAv5sdHMrHcgmLcuH1/0PVNUU+PAp8eOMbELGRSvLhjI0QiujSKTA2Xb9zW+2zb28mxZ8fmaT173XhxHKfXLLO80vSvA5nOdMf9p3uz18FmH21pbRMoG9Oj2/jWxtoKcrnthO7T1sYKqxYm4t++9wp+9MZz2LVxOeYlRMPbw3VUv0eamprA8xo42ssR4OOBmZEh+MMvvovvv7oTs2LDR5wZciLNiApDsM7Y7OFTFwXKhhBCCCGEEEKIqTGN6qSn5dJGY2jNpgG5GPIwDH4ODDgBwRnaecuQkx8GPG6RgSddnjznarUalTX1KCypQFFZJQpLKlBcVonC0goUllaiuUX/5N0rz2/H2jWrRt4FgOc2r0DhO1+ga8BAwcHkYljJxJgT7jb0xgLqUqjwzsls1LWyXRtXLUyCv7fHEFuNQKMyLClz7RBnyGebM/B4ZshzZtTj8hhNct5ubq7YvHY5Tl+8DqWKfZ+WVdXg3U8OYE7CDCydnzT8wJ65vkcNzVujHjTs4+mKisoq7XJFZZX+uoa8z3jN+LcFhsx7wrcFAKnlyOsMxaDjwuDPt1qt/3ha2tqNOzhj8DFl8o9Jgf6+zEx/jzKyER4ShMjwadCle5j3Gc/zyMwtZF5TT3c3w7+z+vdgpPvp197Rgez8YibnpPgZ4Az9LTkQZ9gxydPDHfMTZyL53iNt7PCZK9iwasnIG4sM2Ddvpt/3ALXbImQ4Qv6WJ+bDaN/d49m3ge9RzoBTmoZ+/4sNaOgw2LYymqmZEEIIIYSYlj+/9ylu309jYt/52vNYMj9x2O22r5mP7MJyNLd1aGNHTp5DaJA/PD1Mczz0KZ7nsf/IKTQ1tzDxreuWwcXJQZCcCJkIKpX+eFBrWzs+PXAMG1YthYf79C70Gg8fLw/kF5Vol6nZq+nieZ5mbtTh7OQIaytLdA2YzTWvoBhzEuOES8qE6DZ79XR3G7Rp9kQQiTj4+3jA36f/+qxepQqVNfWob2xBr1IJpUoNtVoNiUQCmbTv7+r1ZEQGuGvzDA70Q3hwwKTkPBKO47Btw0r84Z1/aGOHT13Er3/yHQGzIoQQQgghhBBiKkyjuJGQSVbX0IS3PtiPhxm5KCypQElFNXp7laPe3tfLHX/81Q9Hvb693Aa7NizBPw6dZ+KfXi0AD2CuiRU4dvQo8b+ns1HR2MnEvT1csW7ZHIGyImRixcdGIsjfB2cv30R2fhFzm0bD4/b9x8jIKcDaZQsQFR48aSetzZmPpweAx9rliqpa8DxPz50JcnSwh62NNTo6u7SxKzfu4KXd2wTMSnjzkuLxKD2LmdXy+NlL8PHymPCunKYsIzsPJeWVTMzPx0ugbEbnweMsaDT9BYBSiRQzoyMEzGhw29YvZ4obj5+7BpVKBQnNmE0IIYQQQgghhBBCjKyltQ2FJRUoLC1HUWklCkvKUVxWiZspD5n1ggN88R8/fXPE+7OykGHP5qV4+9OT2piitxdvvfcR/ulbX4OLs6PRH4Mx8DyPI6fO427qIyYeGuSHRXNmCZMUIRNk2cI5KKusgkLBNtopr6zGux99idkJM7Bs4VxYWloIlKH50Z25sbq2ns7rm6jeXqXee18mM6CJ1RTAcRz8fLyQM+D6iEvXbyMsJBCODvYCZiY8nuf1xkOFLoaVSSUI9PVEoK/noLdXVNWgo7OTuR5jdvyMyUpvVLavX8EUN2bnFSE7rwiRYUECZkUIIYQQQgghxBTQ2TQy7SgUvVi1+9tIzykY93387Q//Cns7+Zi2SYwNRUNzK05cvquN8Tzw6ZUCdCtUWDbDNIoCWjoUeOdUFqqbu5m4s6M9vvnSdkjpJDyZwuzt5Ni1dS3yi0px+uINNLeys7a2d3TiwInzCMnwxTMbVsHKyoDZ96YBHy93Zrm7pwdNza1wpi7HJkcsFmPhnFk4e/mGNlZUWo7S8kr4+3oLmJmwHOztsG7lEhw9fUEb6+7pwdHTF/DCzq3TslC3p0eBcwPeJ0DfsXNGdLhAGY1MrVbj/uMMJhYbFWaSx/Dt61fgn3/1J+1yY3MLbtx9iGULkgTMihBCCCGEEEIIIUbD831/Rr1PzcjrDEXI81sGPQ9Gfg4niyFpG/BaZeTk4+7DDBSWlKOopAJFpX0Fjc0tbSNuy3EcPvjL72Bj7zSqfUWGh2JBYgyS7/efj2tta8db732I7319H5wcHcb7MCYEz/M4feEqLl5NZuIWMilefGYDRBzG91415HNpyLZ9d2Dg9gbs2djHN7NgwGMW4Pny8nTHN1/Zg3OXryMrt1AnHR53Ux8jMycfa5YvRExk2NDjIIK91gbu16Dj8OAbe3v2zZD29P2vVqtRU1sHH+/+4iPDny5D3mcGHlPM9XM9yHvXwkKq1+z1VsoDBPjpjIWa62MeZ9qJM2OQk9d/POjt7cWRk+fx8nPbIRKJRncn5jpmOsxrnZWTj/KKKibm5eFmxPeH8d9n9x6yM2872MsRGuyvsy9D92vY997s+Bh4ebihqqZOGz5y+iIiQ18bxfaGHM8E/Fybbd4CfveQseFGeaweipDnFHgDtjfocRv6vWXI9mb6nWno8chcfytMR0Y/X2emv60JIYQIwsBftoSYn9+89cG4ChtFIhHCgvzw/p9+jtVL5o5r32sWzcLK+fF68UO3SvD++Vy0d/cOstXk4HkeqQUN+H8HH+sVNtrLbfDmvh1wsJu+s1SR6SU0yB/f3LcLS+cnQSIW691eUFyOAyfOM7OAEX32dnLIbW2YWHlltUDZkJHMiovRe72u3rw7xNrTx8yYSESFhzCxwpIypKQ+HmKLqe1q8l1m0BcA1q1YDJlMJlBGI8stKEZ7Bzsb9ez4WIGyGV6ArxcSYiOZ2OHTlwTKhhBCCCGEEEIIIYSYu9/8+T3MWLYDr/3g3/C7t97HV8fP4f7jzFEVNgLAd19/AYvmja3x1jPrlurNKNTQ2Iw/vvM+ausaxnRfE4nneRw+cQ6nzl9h4hzHYe/OzXChRo1kirK3k2Pn1vV44dnNcHLUn5mto7MLh06cx8f7j6KpuWXyEzQzlpYWcHVmC8ArqmoEyoaMZG7iTGY5r7Bk2r9eIUH+iI+NYmJlFVW4eSdVoIyEp1D06jV7ldvaIDTIX6CMRtbV1Y3MnHwmlhgXM/oC1UkiEomwdd1yJnbkFI2FEkIIIYQYiqM/7R8hxHyZ1r9gCZlgj7Py8Pt3PhrydhtrK8RGhmDr2qX4/uvP4+3f/ginP/0f5N44hK7Cm8i5cQj7dm0e9/45jsPWVfMGLXB8WNSI3+5/hAeFkz+o197di/cv5OIfF/PQ2aNibnO0l+O7r+ykATwy7UilUixdkIRv7tuNkEBfvduLSitw/fb0PaE/GhzHwcfLg4kVlpSjrb0DarVaoKzIUKRSKRbNTWRixWUVKC4tFygj08BxHDauXq5X+HnhajLqGxqNvj+e5/HgcQaOnrqAqzfvoKCoBN3dPeO6r87OLuQVFOPy9dv4+Msj+P3/vIs/vvN3HD9zEaXllWPuol1TW4+7OkWdYcEBCA8NGld+kyXlAdul1NfLEx7urgJlM7Jt69kBvaNnr1AxPSGEEEIIIYQQQggZszupafjlf/113NsvnZ+E3/z422PezkImxTdf3Aovdxcm3tzSij++83c8zsged07G0tHZib9/8hUuXb+ld9uLOzYgLiZcgKwImVwhQf745ivPY9nCOZBI9Ju9FpdW4P1PD6KtvUOA7MyL7nho+TQvljNlsxNmwNrKkolRs1dg7YrFcHRgi52vJt9FVXWtQBkJ69qtFL1j39oViyGVSgXKaGQP0jKhUvVfgyEWi/SKVk3F9g0rmOXUtCyUllcNsTYhhBBCCCGEkOlCInQChEwWlUqFr/3zb5iTORKJGG//9keICQ9GcIAP3FycwE3wFOhPCxytbWxw/MJN5raOHhU+uJCHh4WNeHZhIOysJ3YWJJ7n8aCwEV/dLNIragQAN2dHfGffDjg52E1oHoSYMidHe+x5ZiOy84pw6uJ1dHb1z2x67fZ9+Hp7IDhAv/iR9PHxdEd2XqF2OT07D+nZeeA4DjbWVpDLbSG3tdH5s4bcpu//W1tbmVw3waksYWY0bt5NZQZrrt68iwA/nwn/fjRl1tZW2Lp+FT756qg2plKrcOjEObz20i6IB5nhdbweZ2Tj+Fn97pQuTo7w8fKEr7cnfLw84OrixHw2VCoVamrrUVFVg8rqWlRU16C5pXXQfTxIy8SDtEw4OthjZnQEZsZE6g1Y6uJ5HqcuXGUKIiViMdatXGLS7438whKUlFcysSQTnbXxqe3rl+Pnv39Hu1xZXYd7jzIxJ8G08yaEEEIIIYQQQgghpkOh6MXXfvDLERuciUQi+Hp5IDjAB4H+PggO8EWwvy/CQwIQGxk67nN/1laW+Pbe7fjLh4dQXdffJK6jswvvfvgFkhJmYOfW9bCxth7X/RviYXoWvjx0Au0dnXq37d66BnNnzZj0nAgRikQiwZIFsxEbFYYzF68jv6iUub2zqxsHjp3By89tN+pYyFTj4+WBB2mZ2uXpPhOgKZPJZFg4dxbOX0nWxgqKy1BeWQ1fb89htpzaLCxk2L5xNT747KD2t4NGo8Ghk+fwxt7dkMmMf+1SY1Mz6uob4eTooDfuOVZKpRJVNXWoqKpBXX0jLC0tEBEaNK4x7rr6Rty5/4iJBQf6ISo8ZNz5TTSNRoP7jzKYWHREKGxsJv931mgsnjsLTo72aGruH8s+cuYSvvf6iwJmRQghhBBCCCFEaFTcSKaNP//tC6Sm5TCxn3z7Zbz+wvZJz4XjOKxZMgeuzg748vhFdHaxMzI9LGpEemkTZgW7YFG0B/zdbI1aONCtUCElvx43MmtQ09w96Dpx0aF4bvNK2JroyS5CJhPHcYgKD4aNtRU++uoYNJq+E/o8z+PwqYt446VnYSe3FThL0+Tj5T5onOd5dHR2oaOrG9W19UNuLxJxsLV5UvBoa6Mteuz7/1bw9nSHlU53TTJ+EokEi+Ym4tSFq9pYaUUVikvLERTgJ1xiJiA40B9zE+OYwayaunpcuXEHK5cuMNp+dGcZfKqhqRkNTc14lJEFAJBJpfD29ICToz1q6xtQXVs/5hlRm1tacTX5Lq4m34W/jzdmxkQgKjwUlpYWeus+Ss9CeWU1E1s0L3HEokgh5ReW4Mujp5mYjbU1osKDBcpodCJDgxAREoicgmJt7PDpy1TcSAghhBBCCCGEEEJG7T/e+juy8oqY2JJ5iUiYEYUgfx8EB/QVMvr7eEEmm5hZiOzlNvjeq8/ir58cQ2kFe27x3oM05OYX4blnNmFmTOSE7F9XR2cnvjpyGvcfpevdJhJxePHZTZgTHzMpuRBiapwcHfD8jk3IyS/C2Us30NrWrr2tvLIGl67fxuplCwXM0LT5erMzN7a2tePtv30yoLHrk2av8gH/39YGEgldsiWEpPhY3Ep5iI7OLm3savJdvLhzq3BJmQBfb08snpeEa7dStLHGphZcuJqMDauXGXVfhcWl+Ozgce11F33jnu7w8fLQ/g1VmMfzPBqbmlFRVaNt+lpb36C9r6fupj6GvZ0cM6LCERcbCWcnxxHzetrsVaPRaGNisRjrTbzZa3ZeIVpa25nY7ATTbdYgkUiwec0yfPjlUW3syGkqbiSEEEIIIYSQ6Y7OlJFpIb+oDL/8w3tMLDI0ED978xWBMuqTEBOOkAAf7D9+CY+y8pnbVGoed/PqcTevHr6uNlgc7YGEYBdYSMfXEZHneVQ2duFGVg3u5dWjV6UZdD0ba0vs2rQCCTHhJn1yjhAh+Pt6YcWiubhw7bY21tnVjYMnLmDvrs0Q0wyDerw93eHoYD/kDHIj0Wh4tLV3MDMJavE8pFIJntm4ChGhQQZmSp6KnxGFm3dTmYHrKzfvItDfd9p/L6xYPB9FJeWoa+jvNJ6ckoqQIH8E+PkYfP+NTc2oqqkd1bq9SiWKy8pRXFZu8H4BoLSiEqUVlTh94Roiw4IxMyYCQQG+EIlE6OrqxoWrycz6Tg72WDBn1pj20d7egbLKathYW8HDzXXQIkpjySssxv6jZ/QKPuclxpnFxQLb1i/H/3vrfe3y4dOX8LufvTntP4OEEEIIIYQQQgghZGTp2fn43V/eZ2IJMyJx4cB7k35uzNbaCt/92m58uP8E0rILmNva2jvw7odfICYyDMsWzUV4SJBBszYNpbWtHbdSUnHlxl10dOrP1mhtZYm9OzchNjLU6PsmxJxwHIfIsGAE+HrjvY/3o7mlTXvbrZSH8PPxRESoaTcPFIqLsxMsZDIoenu1sadNK4djZWmpXwA58E9uC1sba4jFNAZtTFKpFAvmJODc5ZvaWGFxOUrLK+Hv6y1gZsJbPD8JBcWlqKzuH6+89zAdoUEBCAsJNNp+rianMMWIfeOeFSguq9DGnBzstYWOdnJbVNfWa4sZexSKUe2nta0dN+7cx4079+Hj5YG4mEhER4QO2Tw5LTMHpeWVTGzB7IRRFUYKpbi0HEdPX2Rinu6u8PYcvAm1qdi+fgVT3Hjz7kPU1jfC3dVZuKQIIYQQQgghhAjK9K/sJcRAGo0Gr//oP5iTWxzH4e9/+BksLGQCZtbHztYGX3tuEx5k5GL/iUt6szgCQHl9Jz67WogvrhXCw9Eavq428HO1ha+LLXycrSHTKXjkeR7NHQqU1XeirL4DZfWdKG/oQGePathc4qJDsXvTSshtabZGQoYyPykOZRXVyC0s0cbKKqtx+eZdrFo8T7jETJREIsHOLWtx7dY9VNfWo72jk+l0aCilUoWzl28iPCSQin6MRCKRYPG8JJw4d1kbK6+sRmFxGUKC/AXMTHhSqRTbN67B3z7Zry2a43keR06dxzf27TG4WC8zh210IBH3/VRXqYf//h6OVNLX6fTpX1NLKx6lZw05oK5Sq5CenYv07FzIbW0wIyoctXUN6Opmf5+sX7V0TBdC5eYX4dCJc+hVKrUxB3s5PNxc4eHmCl9vDwQF+Bnlc5xbUISvjp3VK2ycGR2B+bPjDb7/ybBdp7ixsKQc6dn5mBEVJmBWhBBCCCGEEEIIIcTUqdVqfO0Hv4RS2X9OUSwW4+9/+pVgTb8sLSzwxovP4O7DDBw4cRHdPWxBQkZ2HjKy8+Dm6ozF85IwNzEe1tZWBu2T53kUFJXi+u0UPEzLGnJcYkZkKJ7btg72drYG7Y+QqcTKyhLPbl6L9z87CLW6/7Nz9PQlvLHXBY4O9gJmZ5o4jkNQgC+y8wrHtF13Tw+6e3qYppqDcXFyxPJFcxEVQUXYxpIYF4Pkuw90Zm9Mwd7d2wTMSnhisRjbN67G/334JZQDxvSOnbmEb77y/JCzKY5FS2sbyiurR1yvqaUVTS2tSMvKNXifALQzPZ65dA3hIUGIi4lEcIAvxOK+6616ehQ4f4Vt9upgb4dF8xLHtB+e56FSqSCRSCb8+oWiknJ8cfgk87sPAObMmmny106sXDwXtjbW2s8gz/M4dvYKXn9xh8CZEUIIIYQQQggRChU3kinv758fw7U7D5jYt/ftxLxZMwTKSB/HcZgVG4HQQF8cO3cD99NyoNK5IB8ANDxQ1dSFqqYu3M2t18bFIg5SiQgijoNSrYFKpQGvt/XQ3F0csWHFAiTEhJn8CS5ChMZxHLauW453Pz6AlgEz2yWnPIKflwfCjdixcKrwdHfF7m3rAfSdlO7q6kZ7RyfaOzvR3tGl//87OtHR2QWeH92RrKW1HU3NrXB2cpjARzG9xMVG4sade2hp7X+PX02+i+BA4xSfmTMPd1csXzQPF672d3NtbWvH6QtXsX3TmnHfL8/zSNcZnEuYGY01yxeh5kkn0oqqGlRU1ww7E6qrsxN8vDzg7dnXydTN1Vmv2/mCObNQVV2Lx5k5SM/KRXePfmMFAGjv6MSZS9fxMC0Tzo6OcHF2hLurM6Ijw8ZU6HrvQRpOX7ym95luaW1HS2s7cvKLAADREaHYsXktDHmH5eQX4cBx/cLGuJhIbF67fEI6v0+EhNhI+Pt4orSif3D38OnLVNxICCGEEEIIIYQQQob1P3/7DPceZTKxH31rH+JiIgTKqA/HcZibEIuIkAB8fvgsMnL1C4Dq6htx8PhZHDtzCTERofD39Yafrxf8vL1GLHbUaDSoqWtAWUUVyiqqkJtfiOra+iHXt7ayxM7Nq5EUFz3tz3kTMhgvT3esXb4Ipy5c08Z6ehT46ugZvPrCDkjE4mG2np5WLJ6H+oamEWdrHI+GpmYcOXUBvt6ekMupGNsYpFIpFs1LxJmL17Wx4tIKlJRVIMDPR8DMhOfs5Ii1yxcxjXA7u7pw/Oxl7N6+weDvzYzsPENTHJRIxMHNxRme7m6oqqlFbf3gRcNqtQZZuQXIyi2AjZUVYqPCEBcTiQdpmejs6mLWXbdiMaRS6ahzqKyuweGT59HY1AIba2t4uLs8afTqAndXF7g4OxptrLKwuAxfHD4JlYodE40KD8ZMgX/3jYalpQU2rFyM/cfOamOHT12k4kZCCCGEkHHiOEBEp7hAp/kIMW9U3EimtIqqWvzot28xMX8fT/z2x98QKKPh2dna4MVn1mLrmsW4nZqOG/fS0NTSNuJ2ag0Pda9+MeRwRCIOMyJDsHh2HMKCfGngjpAx6OtYuhoffHGE6Vh65MxlvPHis3B0sBMwO9PGcRxsbKxhY2MND7gO+a8JjUaDzq5udAxS+Nje0Yni0gqmW2RxWQUVNxqRWCzGkvmzcezMJW2soqoGBUWlCA0OEC4xEzEvKR75hSUoKa/QxtKychAWEoiYyPEVn9XVN6K+sYmJxUSGQSwWw9vLA95eHpjzJN7R0YnK6lqUV1ajo7MLTo728PHygJeH+6hmj+Q4Tnufq5ctRH5hCR5n5iCvsFivg7lGo4GFhQV6FD3IyS9CeWU1Fs9PglKpHHEwj+d5XLyajOSUB8Ou91RmTj4S42IQOM5B4+y8Ahw4dkbvMcTHRmHTmmVmU9gI9L1G29Yvx3+/95k2duTMZfzbP39dwKwIIYQQQgghhBBCiCkrLCnHz3//DhMLDw7Az7//ukAZ6XOwk+Mbe3fg7sMMHDx5CV3d+o3XlEolHqZn4WF6ljbm4uwIb08PWFlaQiqVQCQSQaVSQalUobGpGeWV1egdMGYwnJlRYdi9dS3N1kjICBLjY1FWWY30rP5CpOraepy9dAMbVy8VLjET5eLshG997QU0NbcwY5rav/b+/69Uje54NZBSpUJBcSniZ0RPQPbT06yZ0Ui++wBt7R3a2JWbd/Hyc97T/vqZhJnRyCssQW5BkTaWW1CEB48zMSsuxqD71i1ujI0MQ0xkGMorq1FRVYPK6looVaohtu4nt7WBj5eH9s/T3RUymUx7e01tPR5lZCM9KxedXd2D3kdnVxfu3H+EC1eTkVtQDB9Pd7g4O0EiESM8JAjhoUGjfly5+UU4eOKc9hqGzq4uFBaXobC4TLuOndwWz2xaA39f71Hf72AKikrx5ZFTgxY2PrNpjdm8f7etX8EUN16+mYLmlja63oYQQgghhBBCpikqbiRT2n+8/SHa2juZ2Lu//ylsbawFymh05LbWWL1kDlYuSkJmXjGupzxGdn7JqGcxG46DnS3mz4rFgqRYONjJjZAtIdOTt6c71ixdgNOXbmhjPT0KHDx5Hl/b84zZnDA2VSKRCHJbG8htbeA5yO2HTpxnZrkrLqtEooEDKYQ1IzoC12/fQ/OAIvsrN+8gJMh/2r+/RSIRtm1cjf/94DP0KBTa+Mlzl+Hr7Qn7cXy/6g7k2dvJ4es92LsfsLW1QXjo2AbUhiKRSBAZHoLI8BB0dnYhIzsPjzNzUFVTCwBwsLdDYlw0qmrq0NVdCXc3F9y4k4rUx5mYmxiHpPgZsLKy1LtflUqFo6cvICM7X+82kYiDRjP4b5rrt+6Nq7gxK7cAB4+f1StsTJgRjU1rlpnle3b7uhVMcWN6dj7yi0oROoZZMwkhhBBCCCGEEELI9MDzPN744b+ju6e/WJDjOPz9T/82qoZok+npLI5x0WG4+yAT1+8+QHVtw7DbNDQ2o6Fx/LOhSSUSJMZFY8ncBPj5DH7elRDC4jgOG1cvQ3VtPfP5u/8oA/4+noiNChcwO9PEcRycnRzh7OSoe4v2//E8D4WiF+0dHYMUQbIxtZotXCouraDiRiOSSCRYNHcWM0NpaXkVSsoqEOjvK2BmwuM4DpvXLsdfP6hhZjM8e/kGAvy8B3mPj05DYxNq6tjv/ISZ0Qj099WOe2o0GtTVN2qLHSuqatDd0wNXZyd4e7nDx7OvmNFuhPFYD3dXrHV3xaqlC1BYXIZHGdnILShimlf359WMjs6+5rIFxaXw9fHCqy88O+rHde9BGk5fvDbiNV1t7R04dOIc3nz9JUgk45sBd6jCxuiIEGzfuBpiM5pZd/3KRbCwkEGh6AXQN7588sI1vPjsJoEzI4QQQgghhBAiBCpuJFPa7ftpzPJLO9Zj9ZK5AmUzdiKRCLERwYiNCIaiV4mK6jqUVdWivKoOZZU1qKlvGvbkmLWVJfy83eHn5dH3X28PONnbmuUF/oSYoqT4GJRWVCEzt1Abq6yuQ1FpBYIDpveAx0TzcHNhihtLyirB8zwd34zo6eyNR09f1MaqaupQ39AEN1dnATMzDfZ2cmxYvQyHTvR3k+xRKHD01AW8tHvbmN+LGTlscWN0ROikv59tbKwxJzEOcxLjUFffgMcZ2UjLykVnVxf8fLzh6uwMC4u+jqdd3T24fOMObt5JRVJ8LOYmxkEu7+t03t3dgy8Pn0RpRZXePpYumINF8xLR0NiMmrp65BeVMAWQxWUVKKuogp+P16jzzszJx6ET5/QKG2fNjMbG1eZZ2AgA8xJnwN3VGbX1jdrYkTNX8KNvvSxcUoQQQgghhBBCCCHEJH159Cwu30xhYt/ctwsLZscLlNHILC0ssGReAhbPjUdBSTmu33mIhxm5euf5DOHq7IjFc2dh3qxYWFtbGe1+CZkuLCxk2LllHf72yVdQKvtnUjtx7go83d3g4jy+AqfpjOM4WFpawNLSAq4uQ4+38TyPlNTHOHOpv/CuuKyCxkONLH5GFG7efYDWtnZt7MrNuwjw85n2z7ONjTW2rFuBzw+d0MaUSiUOnzyPV/bsGFcRnW5TVLmtjd4shiKRCB7urvBwd0VSwozxJT+AWCxGWEggwkIC0d3dg8ycfDzKyEZFVY12nUB/H9jZ2aK4pBwqtRrgeXz05RHExURiwZwEODrYD3rfPM/j4rVbSL6bOup82to78DgjB7Pixl6onF9Ygi+PnNIr0IyJDMX2jashEonGfJ9CsrWxxpql83H83FVt7MjpS1TcSAghhBBCCCHTFBU3mhLO0JMMBgz0GDohoCEn9USGdI0afr+WFmwn0nmJM9nn2ZDnXGTYx4cb4+O2tBQjJNAPIYF+2piitxdNLe1QKpVQqtTQaDSQSiSQSiWwsrSAo71c74Qrr1HBoBecV4+8ztAbG7CtgYQ88WzQZ9vAvM31hLuQaY/hbcpxHDavWY7q2gY0tbRqn++cghIED/isjm6/Bn4+DHmtDf7+MeSYMvptlUolsvOKkJqWhdLyKma/nV1daGpqhrOTw/hzGQuDvrsA8IZcnGHIaz221WdER+DS9dto7+ifBbmuoQluri7jz2EKiY0KR15BMdKzB84iWo7b9x5i/uwEABjVwKdSqURLSxu4AS9QS0sbAM7AgdPxfzbdXF2watkirFiyANm5Bbj74DHKBilW7FUqkZzyAHdSHyMuJhKxUeE4ee4yGprYLuoikQib1izXdhR2d3OFu5sroiPCUFpexbzHbty5jz3Pbhn50fE8MnPycfjkOWh4vu84+OQhJ8bFYsPqpZM78GzocUGHWCTGlnXL8d7HB7SxI2cu40ffedWo+zHstx0g6O87I8xqPi7m+vtKUPScTTqDf9+Zo2n6PhP0mGTI71Izfr2E+v4hhBBCCCFkGCfOX2WW/bw98R8/fVOYZMaI4ziEBvohNNAPrW0dyMgtRFllDcoqa1BZXddXZDBKDvZy+Hl7ws/bAyEBvggJ9INIZMb//iDEBLi5OmPDqqVMM8xepRKXrt/Grm3rBcxsauM4DiFB/sCl/lh7Rycam5rh4uwkXGJTzNPZG08O+B4tq6hGVU0dvD3dhUvMRISFBCIxLhb3H6VrY5XVtbh+6x6WLRpbY3me55GRrd/sdTIL8qysLJEYH4vE+Fg0NDb1NXrNzEVrWzs83VzhYCfXvvYqlQr3H6Uj9XEGYiLDsHDOLLi79Y+Rq1QqHD19Ue8xAUBcbCTCggNRU1uPmroGlFdVo7u7f3btm3dTET8jckyPPa+gGPuPntYrbIyNCsO2DavMrrDxqW3rVzDFjWevJKOzsws2NtbCJUUIIYQQQgghRBBU3EimNHdX9qRubUOTQJlMDAuZDJ5uNHsWIUKysJAhYUYULl6/rY3lFhZj/cpF076bo7HU1TfiQXoWHmfkortHMeR6dIGC8YlEIri7ujCFZ/UNjcNsMf1sWL0MZZVVTEfXS9dvITjAjxngGo5EIoGXhxuqauq0sey8Qly5cRvLF883es5jIRKJEB0ZhujIMJSWV+LmnfvILyrRW0+tViP1cQZSH2fo3WYhk2Hn1vUIDvTXu00ikWDB7Fk4e/m6NpZfVIqq6lp4PRk0VigUaGxqQWNzS99/m5q1/1/R26t3n0nxsVi/apILGyfI9vUrmOLGuw/SUVFVAx8vDwGzIoQQQgghhBBCyLhx3AQ0ejDg/gxqwmYgg/Y9OQ33BmVQo0FDmmMNn/fAC+YB4MVnN0Juaz1gu/HnPdaGrfp3MPrt7e3tsWB2AhY8WVapNaiurUdZZTUamlqgUqmgVKmgUWsgkUoglUhgZWUJX093+Pl4Qm5r039nmidFkeN9zQ15jwr1/jYQP22buQh1TDGfxnFxMREoq6jCg7RMbSy/qAS9vQrIZLIJ3TfD0O9QQ7afxNdapVIhO68QqY8zn+y3f/uS0jK4OA4+i5wx9m1Ugh5TRv9ax8dG4Made2ht69DGKquq4e3hOqH7Nb6J+Y2zetl8FJeVo7GpRRu7ceceQoL84OvtOepdd3Z0oqGRvWbLXm473mwN5uLshBWL52P5onkoKavAnXuPkFdU0v+YnuB5HulZuUjPykVYcCAWzUuEi5MjvjxyCqXllXr3u3TBHCxZMBscxyEqPAQAUFhchk++Oqpdp7mlFelZeZgZEzGqXHPzi/DVsTN6hY0zosOxdf3KMRY2GvoeNW7Du01rlkEsFkP9pJlFT48CZ6/ewjMbV41/P4MxpNmrkMcyg36PGpq3mT7u6cjghqcCNrI0KHdDflMa2tDegG2F6W9uOENf62n7b02BTIHrpQghhExPVNxIpjRXF7a4sW6KFTcSQkxDeHAAU9zY1t6JmroGeLqPZ8CDAH0z2WXlFiI1LQtlFdUjrr9oTgIcHcYykEdGy8XZEQXFpdpl3Rn5pjtLSwts27AaH315WHvhh1qtxqETZ/H63t2QSqUj3gfHcdi2cTXe//QAegYU8F6/lQJnJ0fMjImcsPzHwt/XG/6+3qiprcfNu/eRmZM/4sUuclsb7NmxBR7DHA9nxcXgxp376Ozq0saOnLoAaytLNDa3oKOza8htdc1OmIF1K5dMicJGAFg6PwkO9nK0tPYXzx45fRnf+drzAmZFCCGEEEIIIYQQQkyN7phoU0urQJkYl0Qihq+3B3y9qdkXIUJbtWQ+HmVkQ6PpKzhQqdUoKq1ARGiQwJlNHQ2NTUh9nInHGTno6u4edJ2urp5B42T8xGIx/Ly9kN7WPwNfQyONhz4lk8nwzMbV+PunB6DR9I0LajQ8jpy6gDf27oaFxegKnKVSKSwsZFAo+huXXrx2C95eHvDz8ZqQ3EeD4zgE+vsi0N8XjU3NSL77AI8zc7TFdgPlFRYjr7BY73EAfQ1jN61ZjvgZUXrbBQX4wtvTHZXVtdrYjTv3ERsVxhQm8jyvnaF1YOPXwpIyvcLGmTER2LJuhdnO2PiUk6M9li1IwsXrd7SxI6cuGr+4kRBCCCFkiuMgbKsVU0HPASHmjYobyZTmrlvcWE/FjYSQsWlta8fxc1dQUVWLqLBgrF+5SK9YycXZEU4O9szFArkFJVTcOA61dQ14kJ6Nx5k56OnRn5FtIBtrK8THRCBhRhScxtShlIyFi5Mjs0yDefoC/HwwPykBySmp2lhdQyMuXb+FtSuWjOo+XF2c8eyWdfjswHHthQEAcPzMRTg62MHPx9voeY+Xh7srdmxeh+WL5iH5bioeZWQPOsDn5uKMPc9ugb2dfNj7k0qlmJcUj4vXkrWx+sax/2abM2sm1i6fWrPmymRSbFq1BJ8cPKmNHTl9iYobCSGEEEIIIYQQQgjD3dWZWaYxUUKIsVlZWcLfxwvFZRXaWG5BMRU3GkilUiErtwCpjzMHnQVuIAuZTDsTHDEuFycHZpmavbK8PN2xdMEcXL7RX4DW1NyKc5dvYPO6FaO6DwsLGVYtWYCT569oY2q1GvuPnMLXXtxpEo2MnZ0csXndCixdOAe37z1E6uMM9PYq9dbTLWy0sJBh55b1CA70G/R+OY7D4nlJ+OJw/3hfQ2MzLl2/DYlYjMbmFjQ0NqOppXXQ/emKi43E5rXLzb6w8altG1YyxY0nL1yHQtE76sJZQgghhBBCCCFTw9T4Vy4hQ3B3ZYsba2nmRkLIGHR0duHjr46jsKQcit5ePMzIxuFTF5nCI6DvZHR4SCATyy0snsxUzZpSqcSj9Gy8/9kh/O+H+3E3NW3YwsbgAF/s3LIGP/j6S1i5ZB4VNk4wF2e2uLGxqUXvM0CAZYvmwsONLWi+fe8hikrKRn0fwYH+WL+KLYZUq9U4fOKcST7nTo4O2LR2Bb73xsuYPzsBsgGF30H+fnhlz7MjFjY+lRQfCytLy3HlYWVpgdXLFmLtisVTqrDxqW0bVjLL1++kop5+0xJCCCGEEEIIIYSQAfSKG+n8ESFkjBoam3H09EWcPH8F7R0dg66jOx6aV1hikuMX5qC+oQlnL13HH//6AQ6fPD9sYaO1lRXmJ8Xjm688rzduR4xD93ltaGoRJhETtnDuLPj5eDKxB2lZyM4rHPV9JMbHYs6smUyss6sbnx88gZ4ehVHyNAY7uS3WLF+E773xMpYumAMrq6HHMOW2Ntj33DNDFjY+FRYSqDeWnHz3Aa7duoeM7HzU1DWMqrAxPjZySszYONDWdcuZMd629g5cGlBISwghhBBCCCFkeqCZG8mU5uasM3MjDeQRQkapq6sbH391HI3NLUw8O78IF67dxpplC5h4eHAAbt9/pF2urm1Aa1v7qAt7pqOa2nqkpmUhLStPr7uhLlsba8THRiI+NgJOjg59QZ4GSyeD7syNKrUKrW3tJtE909hy84twK+UBAMDZyQHOTo5wcXKEi7MjHOztIBaLh9xWIpFg+8Y1eO+jL6FSq7TxIyfP45uvvjDsoNdAifEzUFPXgPsP07WxltY2tLV3wMHebpyPbGLJ5bZYvWwRFs1NQlFpGaQSKUKDA8ZUaGhhYYFF85Jw/sqNQW/nOA4O9nI4O/a9Hs6ODtrXyE5u278vnjfGQzIpq5fMg7WVJbq6ewAAGo0Gx89fxavPbxc4M0IIIYQQQgghhBBiKtxc2PO4tQ2NAmVCCDFHVTW1+OjLo1D09o3XFRSV4dUXnoHc1pZZLzwkEGcv95/H7+zqRmV1LXy92YInMjilUqmdpbGsomrE9QP9fTBrZgwiQgIgkdDlXRNJdzy0rb2DZo7TIRKJsG3DKvzfh18yY/snzl6Gj6cH5HLbYbbut2b5IjQ2taCguFQbq29swsnzl7Fj8zqj520Ia2srLF04B/NnxyP1cSZu33uItvb+4m83V2fs2bF5VNeEcByHxfOT8NXR0+PKheM4zE6InZLNXj3dXTEvcSZu3XukjR0+dQnrVy4WLilCCCGEEEIIIZOOzn6RKY1mbiSEjEdPjwKfHDyBuiEG/2/ffwR7uS3mJvZ3FfTz8YSVpQW6B3QUzC0oweyE2AnP19wolUocPHEeuQUlw67HcRyCA3wxa2YUwoIDhi0sIxPHxsYalhYW6FH0v7cbGpunVHEjz/O4cuMOrt9O0cZKK9gOuSKRCE4O9nBxdoKzo0Nfgd2T4kdraysAfQNYi+fPxrHT59HV3YPu7h7Y28nx+/95F9s3rYGLkyOcnRxgYWExbD5KJduVU25rC7tRDggKycrKEtERYePefl5SPEQch9KKSlhZWvYVLz4pYnR0sJ+2A/fW1lZYv2IRDp68oI19euAktq9fCUcH0yx4JYQQQgghhBBCCCGTy92Fnbmxtp6KGwkho1Nb14BPvjquLWwEgJa2Nnx+6CT2PbcdMll/cZejgz3cXJyZMdSc/CIqbhxBXX0jUh9n4HFmzoiz09lYWyMuNhIJM6Lg/LTgjhq+TjhnJwdwHAd+QBPNxuYWeHm4CZjVxGhr78Cj9GwAfY/bxckRTo72kMpGLuR0dLDHuhWLcfT0RW2sq7sHx85cwp5nN4+q6E4kEmHH5rX44PODqBvweyUzpwBb1ikhlUrH8agmlkwmw7ykeCTFxyI9Kw9FpeVwcrDHvKR4WFoOP+47UGRYMFxdnFA/wrVrUqmkv9GrY1+zV19vj/4m0FPQtg0rmeLG4+euQKX6+bQdHyaEEEIIIYSQ6Yj+BUimNDcXtrixpbWduqsRQoalUPTi04MnUF1bP+x6564mw97OFpFhwQD6TsKHBvkjLStPu05uIRU3DuZBWvawhY22NtZImBGJ+NjIKVVAZ644joOrixPKK6u1sYamJoQGBwiXlBEpFAocOXUBOfmFw66n0WjQ0NSMhqZmvdusraxgbydHW3sHOjo7UVxWgabmVgCAu6szyiqroVartQW6clsbZmZI5ydFjw72dujq6kZGdh5z/0kJMyASiYz0iE0Xx3GYmxSPuUlxQqdicrZtWMEUN167fR/uMUuxZN4sbF6zDJtXL4K/j5eAGRJCCCGEEEIIIYQQIemOiba20YxThJCRNTQ24+OvjqG7p0fvturaehw4fg7Pbd/AjFGEhwQyxY25BSVYtXTBpORrbqqqa3Hm0nVmjG0oQQG+fbM0hgZRw1cBSCQSONrboamlVRtraGyecsWNRSXlOHD8rN5nnuM42NvJtY1an45fujg7Qm5rwxQtzoyJQF5hMbJyC9Dbq0R3Tw/Ss3Jx8twVrFwyH1ZWliPmYWlpAV8vT6a40crSwuQL2SQSCeJnRCF+RtS4tuc4Dru3bcCBY2fR0NQEO7mt9jnXFjM66T/n08G2dcvxw3/7g3a5obEZN+8+xNIFSQJmRQghhBBiPjiu72+6o+eAEPNm2mcFCDGQu85AHgDUNTTB19tDgGwIIaZOqVTiiyOnUVFdy8Tt5XLMTojBhWu3tTGe53Ho5EXs3WWt7UYaERLIFDeWlFfSxQODyC0o1otxHIeQQD/MmhmF0CB/GrQzMS5OjmxxY6N+gZ85ampuwZeHTw45S+todXV3o6u7G0Dfezk8JAj3H6VDrVbD3c0VTg72zABUe0cn2js6UVKmPzuklaUF1Or+DrwSiQSJcVQkPd1tWLkYMpkUvb39s3qqVCpcunEXl27cxXf/9XeIiw7H5jVLsWXNUsTFhE+7QU9CCCGEEEIIIYSQ6czd1VkvRmOihJDhNDW34KP9R9DZ1TXkOvlFJTh14So2rl6mPeccERqEG3fua9dpaGpGY1Nz/yyDBADQ29uLzw6eGPb5tbG2RvyMSCTMiJ7SM7KZCxdnR7a4cZCGp+aK53mkPEjDuSs3odHozwTK8zxaWtvR0tqOguIy5jaZTApnRwe4ODvCTm6L1rZ21NU34t7DdHR29Y2Penm44a8ffIZ7D9Nga/O0yavDgMK9vtkhnxYvdnV143FmDrOfWTNjpsXYlrOTI76+7znwvGZaPN7RCgrwRVxMBB5l9L8vtrz0HaxbvhCb1y7D+pWL4GBrLWCGhBBCCCGEEEImGhU3kinN0cEOYrEYarVaG6trbKaBPEKIHpVKhf1Hz6KknC02ktvaYO+uLXBytIdarcHlm3f7t1Gr8MWRM3j1+e1wdnJAcIAvxGKRtjBJrdagoLgM0REhk/pYTJlSqUSZTnfSxLhoLJyTAAd7O4GyIiNxcWYHpKdCcWNRSRkOHDuj15lUJBJhdsIM9PYqnwzItww78DwYCwsZIsOCIZNKAWDUA1MajUY7CPjUzJhIWFtbjWn/ZOqxk9vijZeexV/+/vmQ6zzKzMWjzFz8+5/ehZ+3BzatXoLNq/tmd5TJpJOYLSGEEEIIIYQQQgiZbI4OdpBIJFCpVNpYbX0jjYkSQgbV2taOj/cfQ3tHJxP38fRAY3MLM3aS+jgTDnZ2WDQvEUBfEZOtjTU6OvvHTnILSjB/NhU3DlRUWj7k+FJwoB9mzYxBeEggNXw1IS5OjsgrLNEuT4XxUKDvOohTF67hYXrWuLbv7VWiurYe1bX1TDw4wB9pWX2FaJYWFoiPjYJIJOprClvZrTdjKcdxcLC3g7OjA9QaNfObRSzuG5+dTqiwUd+2DSuY4sb2jk58dfwcvjp+DhKJBEvmJmDzmqXYvGYJ/H28BMyUEEIIIYQQQshEoOJGMqV0dffgcWYeUtOzkfo4G/fTspjCRqBvII8QYlw8z6OnRwGO48BxHESip/8VaWOmTK3+/+zddXQc59UG8GdBzMzMsszMzMwxhrFJkzRt2i+FpNymTZomaZidOMaY2ZaZUbZl2ZLFzEyL3x+y1x6xNJJGKz2/c3TseYfugnZX+869V4stuw42qEJoaWGBtUvnwdHBDgAwdsRglJSW48pDX/xXVVfjh6278eTKRbCyskCArxfuJqcb1t9JTGFy40NS07Og0Tx4XZbLZZgybiTMzc0kjIpa4lyv2m56VjYuXI7B0EH9uv3vd316vR4XLsfgwNGTDSqTWlpYYOn8mQjw8xGMV1fXoKCoCIVFJSgoLEZhcQkKCotQVFLa4HMGUJcgGRzgDydHe1RVVcPS0gKFRcUoKS2HXq9vU7wjhgxo822knuk/f3odwwb2xYZt+3D45DnU1qqa3DYtMwf/+3oj/vf1RtjZWmPWpDH4/avPIDwkoAsjJiIiIiIiIiKiriKTyeDq7ICsnAeJB3mFRRJGRETdVUVFJb7buB0lZWWCcW8Pd6xZNh+5+QX4buMOaLQPEo+OnDwLO1sb9OsTBplMhrDgAFyOiTWsv3M3GaOGDeyy22AMEuvNO1tYmGPIgCgM6tcHDvZ2EkVFzXF2tBcs94TOjRUVldi4fR/Ss7IbrHN1dkKtSoXSsvJ2HdvRwQ6+3p6wMDeDo4N9i4U29Xo9iktKUfxQd8z7+oSFwMbGul1xUM+xevEc/O29zxudA9VoNDhy6gKOnLqAl3//Ngb0CcO86RMwf/oEDIgKM7prFoiIiIiIiKghJjeSUdPpdNi6Jxr7jp7G5etxuBWf3GiSwcOKS9v3xRwRNS4nNx8bd+xv9Evoh91PdBQkPqJhImTdv3VJb3XrhfsJtq2/XP9YLayXyQC5TI7c/EIkpqQL4jU3M8PaZfPg4uxoGJPJZJg9dRzKKioEiZBFJaXYsH0fHl+xAGFBAYLkxvjEFGi1WlbdvKf+/ezt6c7ERiPg6uIkWNbpdNh7+BjSMrMwd/okmJkZx2Oo0Wiw99AxXLke22Cdq7MTViye2+iEsoWFOXy8POHjJawAqdPpUFJahoLCYpRXVMLG2gqODnZwsLcz/M4/PJGi0WhQXFJqSJAsLCq+9/9iVFUJO0gCwKD+UXBxdmowTr2TXC7HqsWzsWrxbFRUVuHgsTPYuf8odh8+gaLipt+DS8sq8OP2/di+/xiSzu+GmwufU0REREREREREPYFer0dSagYuX7+FSzG3UF4h7BDGgq9EVF9VVTW+27QdhcUlgnF3VxesWjIXZmam8PX2xKI5U7F5535BwcYd+4/AxtoKAX7eCAv2FyQ3pmVmG4o9Up36c6Jjhg/G6OGDJYqGWsPZSVjstbC4BMmpGQjw85YoInGycnKxYdtelJVXNFg3YfQwjB81DDKZDCqVCkXFpSgofrjIa92/KpW62XME+vlAqVSgrLwCcrm8QWHZ1ho5lMnRBAT4eWPnug/w1tsf4dzl680WDb4WewfXYu/gT+9+Cl8vd8ydNh4rFszAqKEDui5gIiIiIiIi6lDdJLlRdu+no7StI04DMrmIU7fvi5q687Z/1zoi4hZ7blEVkNq/7y/eehfvf/lj688kkyEqLOjBgMK03eeGXOSvj4jnmV7b/BeIzdJpWt6ms/ZvY7eqBsQ8z8T8XosmJu7uX13s8MlzKC4tazFWnV4P6PXQtuZlUuxzRRQZzMxMsWbZPLi7uTRYq1AqsXTeDHz94zbk5BUYxtOzcnHnbgrCggOw5/AJw3hNrQppGTktT3qIfazFPMfFnrsNj1diSoZgOcjPW7rHW8ztFhuzrvlE/GbJRJxb3r4kWwd7O0SGBePWnbuC8Ztx8cjJzceyBbMbJEDWJ2W1xKLiEly9HotrN+JQXlEJWb3X5fCQQCycM63NSZoKhQJOjg5wqtfZUujBuZRKE7g4O8PF2bnBVtXVNXXJjkXFKCktg4O9HaIiQtsUT3PnbvOeEr79iPr1kvLzdBee2trGBovmTseiudOh0Whw+txl7DhwFDv3H0VSakaj+1TX1GDf0bN47JH5whUi/nypI+F7dvf/mNQEow28/YzgM23TjDR2o77PJdIr7zNjvs0i3n9E//0h5d+rRERERNSbFRaVIPr0BVy+HofLMbdw5UYcikvKmtw+r8D4O04RdSdpGVk4efYiyisqGxRaFRRZldUv6NrIMuoXdm2kGCya2LeRQq+tKQYrk8kQffIc8vKFXV1dnByxZuk8WFiYG8Yiw4IxbcJoHDh6yjCm1WqxcftePLlqCQJ8vWGiVEKtqbt2QK/XIyEpFf2jwrvksejuiopLUFQvgTTI31eaYKjVnOvN92m1Ony3aQcmjh6GsSOHGFVnuJtx8di+94igAysAmChNsHD2FESGBRvGTE1N4e7mAnd3V8G2er0e5RWVKCism78sLCpGRWUVbKyt4ORgf2+O1B62NtaQyeTQarUoKS1DYVGJIEGysKiuSGxTggP84FHv3NR7TR0/ClPHj0JOXgF2HTiGnfuP4vDJc412c7wvLTMH//t6I/739Ub8/Y2f49cvPt6FERMREREREVFH6SbJjUTts2XP4VZtJ5fLERUWhF8+vwb9+4hNFCCihzU3cW6MTEyUWLloNrw83JrcxszMFKuWzMEX329BadmDSoexdxIRERoEDzcXZOfmG8bvJKYYbUXHjlRWXoG8AmGl6CB/H4miobZaMm8Gjhw/i9MXLgvGC4qK8fl3GzF72gQM6BspUXQNqdVqxMUn4krMTaSkZTa53fjRwzBhzAjJJyQtLMzh7eUBby8PSeMg46NUKjF+1BCMHzUE77z1S8Tevnsv0fEYLl67Kdi2pKxnvWcTERERERERkUhiiraK2Vf0uSUsONHOAnJ12l/07siJs1j89OsoK286OaA+VydbQHevYKqpdbvPLb7gq5hCg1I+R0UUKaQuJvI1oRWvKRqNBj9s3tFsckU7TtyBx2rrqevO7ehgh7XL5sHK0rzB78yIwf1QUlqK85evG8Zqamqw/8gJrFk2F0H+3ridkGxYdzshsQuuxZCyoHHrH6+klDTBspWlOdxcHNr/uiJpYWApC0yJ0fb3HksLMwT6egmKSOr1ekSfPIf0zGwsnDWlFd1JpbvNer0eyakZuHLjFm7GJTRYb29ni0cWzKwr8NzYe2S90GUAbK0sYGtlgUBfz2ZOXHcshVwGJwc7ODnYAfATbFJbq0JhcbEh8bGwqASlZRVwdnLAtIljJX6uGCFR95eEhd/aELe7ixOeXr0YT69ejIrKKhw8dgY790dj9+GTKCoubXK/f3/8HV7/2WMN5/61YhoBSPl+LbpKrYhzi7ndxhq3SFJdcyJlswmxn83ExC7R3+Z1pLrPxDZOEPO3ubhT984is0Rdq6PbjBkr3gdExo3JjWTUwoP8kZWTLxiTy+WICPHH4L4RGNw/AoP7RqB/n1BYtfglIxG1h1zecz4OKpVKrFg4C34+zXw5f4+NtRXGDB+EPYcedGmMT0yBWq1GeHCAMLnxbjKmTxwtefKU1BLrTeSZm5vBk1UYjYZcLsfUiWPg6+2JbXsOoqa21rBOrVFj+95DSMvIwswp42FiYiJZnNk5ebhyPRY3bt1BTU1tk9uZmCixYNZU9BHdHZGo+5DJZIiKCEFURAh++8ozmLfmRex+6H2qqrpGwuiIiIiIiIiIiKi9/vHhN21KbJw9eQxWLpjRiRER9S4FhcUdnNgoPTtbG6xdNh821laNrpfJZJg+cQxKyypwOyHJMJ6Umo6KikqEBQcIkhsTU9Kh0WigVPIypMSUdMFykL9Pr58nNhZL503HT3sOIyEpVTCekJSKT7/bhKXzpsPb012i6BpXVl6Bazdv4+qNOBSXNJ705efjiWXzZsDKyrKLo6tjZmYKT3c3eLo3UmBaJmXSMhkDaytLLJo9BYtmToRGo8Hpi9ew48Ax7DxwXJCMDACFxSVQqdQwMzOVKFoiIiIiIiJqL36rSEbtubWLEX36omDswPoPMXnsMIkiIup9FAphJaTpE0cjPCQQer0eOp3+3r+6un/19ZYbrNdBrwf0Ol3j26CRfZpY3+g2+ibOq9PDzMwUA/tGwNPdpdW3PSIkEHsPn4T+XiUplVqNxJR0hAUH4OjpC4btikvLkF9QBFcXp465041U/Ym8QD9vyOWcrDA2YSGBePaxFdi8Yx+ycnIF665cj0VWTh6WLZgFRwf7LoupuroGN27dwZXrscjJzW9xex8vD8yeNrGuMilRD2ZlKZykZnIjEREREREREZFxsre1aXKds6M9hvSPwKC+ERjSPwKD+4bDx6t7JV4QUfdiY22FR5fPh72dbbPbyeVyLJo9Bf/+6GuoVHWdYPV6PeISkhAZGgSZTPZgnlSlRnJaJkIC/Zo7ZI+n1WqRlFo/udFXomiorSwszLFy8RycOn8Z0SfPG57fAFBaVo6vf9yGaRNGYdigfpImrGq1Wty5m4yrN+JwNzlNEGd9QwZEYebksQ2u6yAyRkqlEuNHDsH4kUPwzpuv4dSFqxi/8EnBNlXVNUxuJCIiIiIiMkJMbiSjtmDGBPh6uSMtM8cw9sX6bUxuJOpC9ZPTzM3M4GBvJ+6gzXz53vlaf25rayv4+XggJS3LMBZ7JxGLZk+BnY0NSsvLDeN3ElN6dXKjTqdDUoqwah4n8oyXg70dnli1BAeiT+Li1euCdTl5+fj0mx+xYNZURIQFd9g5dTodSsvKUVRcguKSUhQVl6K4pPRe8nAhtFpds/tbWJijf59wDOrfB64uzh0WF1F3ZmFhLlhmciMRERERERERkXFavWQWtu6NFoy98fMn8MzqhfDxdGNHMKJO1lhS0LwZk6HXN1Xgta6gq2AZ9ZZ1D4q3NlWctfnirbpmC8QKzn+/wKxeD1dnR8yaPK7VRSpNTU0RGuSPm3EJhrFbdxIxdGBf+Hi5Iy0j2zB+525yr09uzMzObdDlM9DfR6JoqD1kMhnGjhgCbw93bN19EBWVVYZ1Wq0W+46cRFpmNuZNn9TlCVT5BUW4eiMOMbG3UVlV3ey2JiZKTJ84BkMGRHVRdERdSyaToW94SIPxquoaONg3n7xPRERERERE3Q+TG8moKZVKvPjEcrz+5/8axrbsicbbmTmsSErURRT1khu1uuYTjHqayNAgQXJjfGIKtFotwoL9ceHqDcP4nbvJGDtisBQhSqqsvAJXb8ThyvVbqKoWTrAEcSLPqCmVSsyeNhG+3p7Ytf8IVGq1YV2tSoWN2/dg5NCBmDJ+tKhKoAmJyThy4izy8guha+Pri0wmQ6C/Dwb164OwkEAolfzoS72LJZMbiYiIiIiIiIh6hDlTxiDA1xPJD81H3L6bDF/OhxJ1Cbm8YQJxvz5hUCpFdEITXexVxP5tPHefsGBBcmNKehYqK6sQFuRfL7kxBbOn6nt1wnVicppg2c3FCTbWVhJFQ2IE+Hnj2UeXY8uuA0hNzxKsi719Fzl5BVg2bwbcXDu+qKpWq0VJaTmKSkpQXFKGopJSZGTlIiMrp8V9XZ2dMKhfJPpFhsLS0qLDYyPqTurPhQJocF0KERERERERGQde4U1G78lH5uOP73xmqEqm1Wrx0Xdb8Pf/e1HiyIh6h/pJS21NPjJ2EaFB2HfkFPT3JgFrVSokpqQ3SG7MyM5FeUVlr5i80ul0uJuchssxsYhPTDHcNw9zcrSHvZ0toO9dz5eeqG9kGNxdnbFp+17kFxYJ1p29eBUZWTlYMm9m3ePdRsUlpdi4bQ80Gm2b9rOztcHAfpEY0DeyXecl6inqT+hVt1DFl4iIiIiIiIiIuieFQoGXnliOX7z1H8PY9v3HkZyWiQBfLwkjI+odGiviWDcnKiK50YgEB/jC1NQEKlVdoUu9Xo+4hCSEBQfg0PGzhu3KKyqRnZsPT3dXqUKVTGlZOa5ev4WLD80PA0BQgK9EEVFHsLG2wqPLFyD65DmcOn9FsK6wqASff78Fs6eOw8C+kaLOU1ZegXOXYpCdm4/i0jKUlpU3OsfeFDMzU0SFh2BQv0h4urv26gRj6l1MTJRQKBTQah9cT8Bir0RERERERMaJyY1k9BzsbbF26Rx8/O1mw9jH327GL59bAycHOwkjI+od6lcqffhLw97AxtoKvt4egmqNt+ITMW/6RJiZmqJWpTKMxyemYHD/PlKE2SVKy8oNXRrLyiua3XZI/6guioq6gouzE55euxy7DxzF9Vu3BevSM7Px6Tc/YvHc6QgO9G/TcQ8fO93qxEaFQo7wkCAM6t8HAX4+kNfrKkvUGzVIbqyplSgSIiIiIiIiIiIS64lH5uHNf3+G8opKAHWJVR9+vQnvvPmqxJER9XyNzTlotb2ngKeJiQlCAv0Qe/uuYezWnUQMGRAFJ0d7FBaVGMbv3E3uNcmNOp0OCYkpuBwTi4Sk1EaT0YID/CSIjDqSXC7HlPGj4OvtiW17DqO65kHilEajwY590UjLyMasKeNgYmLS5uOr1Wp8u3G74PeotXy9PTCobyQiw4Jgamra5v2JjJ1MJoOlhbnh8zHA5EYiIiLqneSyup/ejvcBkXFjciMZvdpaFcxMhV8QlpVX4mf/9w/84tlVGNwvotFKikTUMRp2bmx9BcGeIjI0SJDceDshGXOnTUBIoB9u3k4wjN+52/OSG2trVbiblIKY2DtNTto9zNPdFSMG90ffyNAuipC6iqmpKRbOmQY/Hy/sO3wcGq3GsK6quho/bN6JsSOHoF+fcNhYW8HMzKzZ46VlZCH2od+f+8zMTOHoYAcHOzs4OtjDwd4Wjg72cHd1gUW9RC6i3q5+ciMn84iIiIiIiIiIjJetjTUeXz4X73+5wTD2xfrt+MOrT8HO1kbCyIh6PkUjyY11nRt7jz5hwYLkxpT0TFRVVSMsyB9niq4Zxu/cTcHEMcMliLDrlJaV40pMLK7eiGu24Ov9IrnUM4QG+ePZR5dh0479yMrJE6y7eiMOWTn5WDZ/Bpwc7dt03HOXY9qU2GhlaYEBUREY2DcCzk4ObToXUU/E5EYiIiIiIqKegcmNZLT0ej12HTqB1/74HySmZDRYX1hSir3Rp3Ex5hamjx+J4AAfCaIk6vnqVyrtbZ0bASAiNBD7jpw0LNeqVEhKzUBYkL8guTEpNQMqlRqmpm2v2NidVFRUIj4xBbfvJiMpJR0ajabZ7U1NTdAvMgyD+kX2mkqtvZVMJsPgAVHwdHfFph17UVxSalin1+tx4sxFnDhzEUDd88LG2urej/VD/7eCjY01DkafFBzbwsIczz2+ErY21pDJWGKHqDUsLSwEy5zMIyIiIiIiIiIybtPGjxAkN5ZXVOH53/wDf/u/n8Hfx1PCyIh6tkY7N/ay5MaQQD+YmCihVtfNC+p0esQlJCEsOABnLl4zbJeTV4CS0jLY29lKFGnn0Gg0uJucisvXYnE3Oa3Fgq9eHm6YPXU8lEoloO9dz5WezN7OFk+sXISDx07jwpUbgnW5+QX4bN0mzJ8xCZFhwa06XkVFJU6eu9zkeksLi7pCr/Z2cLC3hZeHG4IDfFngneghLPZKRERERETUMzC5kYzSrfgk/OKtd3Hw+LlG11tZWiAqLAgAkF9YjO9/2ouQAF9MnzASzm2skkZEzatfqVTXwkROT2RrYw1fbw+kZTzo3njrTiKmTxwNuVyOisoqWFqYQ6Otm/Rq7WRGd1JUXILbCcm4nZCE9KycFifsgLoujUMGRCEqPBimpqZdECV1Fx7urnj20RXYvvcQbickNrqNSqVGYVFJqyuRThg9nNXHidqo4WRetUSREBERERERERGRGMUlZfjju5/jf99sbrDuTmIqvtm0GxHB/pg6bjgcHewkiJCoZ1Mo2LnRxMQEIYF+uHXnwbzPrTuJWLVkDiwtzAXJJHfupmD44H5ShNmhampqcTc5FbcTkpCQlIraWlWz25uZmRoKvnqw4GuPpVQqMWvKePh6eWLngWioVGrDutpaFTbt2A8Lc/OHCrtaCou83vuxtrJE9Knzgv1lMhnmTp8IDzcXONjZwtzcTIqbSGRUmNxIRERERETUMzC5kYxKcUkZ3nr3M3z0zeYmu8NNGDUYg/qGw9rKUjCekJyGxNQMDO0fiQkjB8Oi3pcbRNQ+7NxYJzI0SJDcePtuMiaOGYbcvELEJSRicP8+KCopxTcbtuPPv/k5zMy6d7KfXq9HTm4+bt+tS2jMzS9s1X5mZqboGxGKwf37wMPNpZOjpO7M3NwMyxfOxrlLV3Ho2Gnode1PfHZytMeQgX07MDqi3sHSkpN5RERERERERETGTKvV4vMftuP3b3+MwuLSRrcJDvABAMTdTUF8UhpGDO6LccMHdvt5CCJj0liXtN44J9onLFiQ3JicloGamlqEBvnj6o04AHXJWXfuJhttcmN5eQXu3JsfTU7LgFbbchKrt6c7Bvfvgz4s+NqrREWEwN3VGZt27EdegXAuvbqmBtU1NQ3GW9K/T11yLBG1HpMbiYiIiIiIeobukdwok9f9dBsyEbs2/FK71fQiv/yWiYhbUi0nO2g0Gnz+w3b84V+fNDlxN2JwP/z3L7/G0AFRSE3Pwv6jp5GdVyDYRqcHzl+Lw/XbSZgwagiGDB3a6EREa8jEPmd1Ih5vnbrlbZrcV9P+fQFAyq58Yu5zSV9jjPV3s3Xq/w7pdHqIvs1S3mXtfIpHhAZhf/Qpw3JNrQoffLkeKrUatjbWyC8shquTIywtLbBuyy6sXjK3XqVFsfeZmOd43Y3W6XRIy8hGXHwibt9NQmlZRav2lstlCPD1Rp/w4HpdGltxZ4p57xL9cmSkXUb1IioBi30Nb+PjJZPJMHLoIHh7uuP46QvIzM5FTU1tm087beJYKBTd42OrUZH096v9RIUtNm5RL8Vi37zEfM5p/IZbWgiLfVRV1zR8v2hiX+MgJvae/fmsSUb7N6OEJLvPJHysJH2e9NbbLRUj/MOrOxDz3V9jt1vBix6JiIiIqHHHzlzGK394B9fjEhpdb2drjSljhyEiJMAwptXpcPpiDK7FxmPS6CEYGBXWoEglEbVdY79HrUl662lCAv2gVCqh0dRdc6DT6XEnMQVuLo6IuXkbDg528PF0x/7oU5g0dji8Pd0ljrh1CouKcTshCbcTkpCRlQt9KyYbzMxM0b9POAb1i4Q7C772Ws5ODnhq9WLsOXQCMbG3RR3LxESJyeNGdlBkRL0HkxuJiIiI6mb+e+MVD/XxPiAybrxKnLq9o6cv4dU3321y4s7T3QX//N2rWLloFmT3Lkb08/HEM2uW4NrN2zhy6gIqKqsE+1TX1GJf9GlcvJGA6ZPGIiTIv7NvBlGP1bBzY++byAMAO1sbeHu6ISMr1zCm0Wjh5eEGpVKBuPgkKBUKWFpaICMrF99u3IE1S+fC0tJCwqgBtVqNxJQ03E5Iwp27yahuZeKZqYkJQgL9EB4SiJAAn3qJmkRCPl6eWLN8IYC651x5RSUqKqtQXl6B8orKh34eLNfWqqBQyDF6+GCEBge0cAYiagwn84iIiIiIiIiIjE9KehZe//P72LLnSKPrzcxM8cvnVuNXz69BfFIaok9favC9T2VVNXYdOokLV2MxY+JIBASHdUXoRD2WTCaDXC6HTvdgHvTh//cWpqamCAn0Q1z8g+6Nx89cRGlZOczMTFFbq0JhcQl0Oh2++H4rnn9sOdxcnSWMuHF6vR7ZOXmIu5fQmF9Q1Kr9ZDIZfLzcMahfH0SGBbFLIwGo+71YMGsy/Hw8ceTEWVRWVbfrOGOGD4aNtVUHR0fU83E+lIiIiIiIqGdgciN1W8lpmXj9z+9j697oRtebmZnil88/il+/+ASsrSwbrJfJZBjYNwKRoUE4deEqzl6KgUYr7JZYUFSMH7bsRHCAH6ZPGgMXZ6dOuS1EPZlCIUxuLCopQX5BEZwc7XtdNeA+YcGC5Mb7rCwtYGVpAVMTE8NYdm4+vtmwHWuXzYO1BJMUlZVV2B99ErcTkqHWtK4brJWlBcKCAxAeEoBAPx8olfc+RojpJEi9jomJCRwd7OHoYN/sdiqVCgqFot0dlokIsLTkZB4RERERERFRt6LXd/z3qWKOp9OIOK/Ibtx6bcvbdMa5xXZt13VeF/LKqmr846N1+Pen61Fbq2p0m8Wzp+DtP7yKAF8vAMCQgQ6IigjHiXOXcf7KTWh1wvs1t7AE327Zh4jwFEybNLbF72UbJRM51yPm8RL1HBX3u9aazm1Un0T3mejHqnX7y+UyPJzPqNVpRZ5b7OuoNOfuExaEuPi7huWS0jIAQHhIAG4nJCMjKwcDosIB6PHNhu1Ys3QuPN1dHxxA7OtwO2m1WqSmZ9Z1aLybjLLyilbtp1DIEejng/Bgf4QF+QvndVv9GiX2Not4rKWcx5XydVTMqdvxHJUBGNQ3DP0jQ1BYVILyynuFXiuq6hV6rUJ5ZSW09a5d8vF0x6gh/cQ9XqJ/t6TqcSL2eSImbimfoxK9f4h9TRCzfyf9/WBZrxB3VXVVw23FfKZEb30dlfKzsJFegyTqLhPxN5dMwtdRsc8TuYjrgXQivlMQc15A5FuPmPd6sX+bi9hXwo+zokj0t4fkJPuc0ci+/G6FiIjagMmN1O1UVlXj7x98g3c+/b7pibtZk/D273+OAP+WOzmZmZli8tjhGNwvAodOnEPsncQG29xNTkXS12kYOrAfxo8aJnknNSJjIq/3h3N8YgriE1OgVCjh7OQANxcnuDo7wc3FCW6uTrC2sjJ0We1pIsOCcODo6Qbjwwf3x4tPrsLW3YdQXlFpGM8rKMLX9xIc7WxtuyxOvV6PzTv3IyU9s8VtHexsEREahPCQAHh7uve6hFWSDqvdEolnaSH8TMvkRiIiIiIiIiKi7kev1+PHHYfw67/9D5k5+Y1u0zciBO/9+XVMHD20wTpzczNMmzAKg/tF4uDxs7iTmNJgm9sJSUhISsGIIQMxduRQmNe7CJyIWqZQKKDRPLiIWqVq/FqGni4k0A9KhRIarTBRRKfVobq6BiqNGrW1Kpibm6G6pgbfbtyBVYvnwNfbQ5J4M7Nzcf5yDOITU1BTW9uqfcxMTREa5I/wkAAEB/jBzMyUxV6pVRQKBVxdnODq0nRhdb1ej+rqmrpkx8oqmCiV8PJwfVBYmIjahJ0biYiIiIiIegZ+M0Ldhl6vx/pt+/Gbv36IzJy8RrfpGxGM9/74GiaOHtLmY0MmR0RYGEzNrXD6wmXkFRRBJldCIZdDLpfDwsIMZy5cwfXY25gwejiGDOzLblFErWBm1ngCkkarQU5ePnLyhBPxFubmDxIeXR8kPvaERCY7WxsM7BuBqzfiAADWVpaYM208wkMCAQCPr1iAbzfuQGnZg0qghUUl+PrHbXh0+QI42Nt1SZxXr99qNrHRw80F4cEBCA8JhKuLU49NRiUi6m00Gg3UajVMHuokTERERERERERE0rkUE4dX3noPZy7faHS9k4Md/vzLZ/DU2uUtJj04OdpjxcKZSErNwP6jp5FXUCRYr9XqcPr8ZVy7cQsTx47AoP5RLGhI1AZyuXC+7NsN22FvayOY73R1doKTo32Pvs7AzMwUwQE+uH03WTCuNFHiqdVLkJtfILjeo1alwrrNO7Fy0WwE+Hl3aayZ2bn4av3WBl3yGmNtZYnwkACEBwfC39eLiWbUaWQyGSwtLWBpaQE3qYMh6gEsmNxIRERERETUI/DbOJJcWXkFftx+EJ99/xOu3rzT6DZODnb48+vP46mV81v1JXJFVTWS0nORlpWPtOy6n/LKasN6vd4E1VolMlIzoFarBfvaWFng3JVY+Hp5YP7MyRg/aggUCk7sETUlIjQQZy5ebdWkEABU19QgJT1TkFxnZmqK2VMnoF+fsM4Ks8vMnjoOwQG+0Gq1CA3yF1Q/dnSwx+MrFuK7TTtRVFxqGC8pLcfXP27D2mXz4ezk0KnxVVRU4uAxYXdJuVwGP28vhIcEICw4APZ2XddFkoiIOkdZeQWefOX3Dca1Wh2Y20hEREREREREJK2cvEK88fYn+GbTnkbXKxQK/OzRxXjzlSfgYG8LtCHJJsDXC8vnz8TZy9dx8vxVVFZVQaYwvVfwVYZatRq7DxzFxas3MGPyOAT4+XTUzSLq0ZQKJQBh57+SsjKUlJXhzkOJfgqFAs6ODnBzcYKbizNcnR3h5uoMG2urHlNQdNjAvoLkRmdHB8ybPhG+3h5QqdTYuGMfElPSDevVGg1+2LoHyxfMQEiQf5fEqNVqsXN/dLNz2E4O9ggPCUBEaBC8PNx6zONDRNSbWNTrSM7kRiIiIiIiIuPE5EaShF6vx5lLMfhy/Q5s2nWoyS8WFAoFXnhsCd589Wk4OjTf0Uyv1yMhNQsnLsUi5nYKdDpdk9vKZDI4OzvDwcEB2dnZyM3NNWxfXlmN8spqZObk4+zl63B3c8UjC2dh1uTRsLe1bv+NJuqhPN3d8NSqJbh5OwG5+YXIzS9AeUVlm45Rq1Jh7+Hj6BsZavSTRkqlEn3Cg5tcb29nW5fguHEH8guLDeNl5RX4+sefsHbZfLi5OndafPujT6KmVjjxunLRHAQH+nXaOYmIqGvV1NRi4aM/N3QSvm/CqKGCpHsiIiIiIiIiIupaKenZ+GLDTnzw9WaUV1Q1us3UsUPxnzdfQWRoQIvH0+v1KCqtqCv2mpWP9OwCpGUXoPLe3KtGI0d2Xjlyc3Oh1+sN+5mbmcLOxhqnLlzFgKgILJ0/A/4+Xh1zI4l6KD8fT9yMS2hxO61Wi9z8AuTmFwB4UNzZwtwckWFBmDl5nNF3BQz098GSOdNw+24yvD3cMGRAH8NtMjU1wYqFs7BpxwHEJ6UY9tFoNdiwfR+WzJ2GiNCgTo/x7MVr9x4DIS8PV4QHByI8JBDOTg5GPzdNRNSb3U1Ox4/bDwjGamtVEkVDREREJB2ZrO6nt+N9QGTcjPsbUzI6+YXF+G7zHnz543bcvpvS7LZTxw3Hf/74C0SGBja7XXVNLc5fj8fJS7eQU1Dc7Lb1KRQKeHt7w8XFBRkZGSgqKmqwTU5uHv776bf4cds+zJg8FlPHDkVIgA/fAIke4uHuCg93V8NyVVU18goKkZtfWPdvXt2/qnqdUh9WU1sLnU4HhULRFSFLysbaCo89sgDrNu9CTt6DSbXKqmp8s2E71iybB8+H7s+OEp+YjJu3hZOu/SLDmNhIRNSDaLVarH7h1zh6+oJgPMDXGz988rZEURERERERERER9V61tSrsOHgSX27YicOnLgmSDB8W5OeFd//wMuZMGd1isk1FVTXOXr2DU1fiUFBc1uR2SqUSPj4+cHFxQXp6OkpKSgAANbUq1NQWIbegCPFJ6di86xCiIsOwZuk8jB7SD6amJu2+vUQ91eypE2BpYYHktAwUFhVDp2v8d7kp1TU1uBwTCzsbG4wbNbSTouw6UREhiIoIaXSdUqnE8gUzsHX3IdyKTzSMa7VabN55AAtnTUHfyNBOi62ouATH6n1H7ubijJWLZ8PO1qbTzktERF0nO7cA01e+iLwC4bV+A6LCJIqIiIiIiIiIxGByI3U6nU6Hwycv4Mv127H9wDGo1Zpmtw/298E7b76COVPHNjtxp9XqcPDUZRw8fRWqFo55n7mZGZydHGBiooRSqYRep4dao0F1TQ3Mzc1RVlaG9PR0VFYKu87p9Xrk5ubi+43bEH3yPAb0jcSqhdMR6OuFmppa1KpUsLWxhlwub1UcRD2dpaUF/H294e/rbRjT6/UoKS2rS3jML0RyWgaS0zIM683NzHpFYuN9VlaWeHT5fHy/ZRcys/MM49U1Nfh243asXjIXPl4eHXY+lUqFPYeOC8YszM0xfeKYDjsHERFJS6/X44Vf/wU/7TksGHd1dsSBTZ/Bw81FosiIiIiIiIiIiHqfuIQUfLFhJ9Zt3Y+CopImt7O2ssTvfv4YXn5iGczMTJvcTq/XIyUzDycuxeJKbBI0Wm2rYzE3N0dISAjKysqQlpaG6urqBse+EXsbb9y5i6DAAMybMRHjRwyEm7Njq89B1NNZWJhj1tTxAACNRoOCwmLk5hUYCr7m5hegvKKyhaMA+YUNCy73RAqFAkvmTsOO/dGIiX3QwVKn0+OnPYeh1mgwqF9kh59Xr9dj98Fj0GgfXEMik8kwb8YkJjYSEfUQJaXlmLnqJSSnZQrGJ44agpceXy5RVERERERERCQGkxup02Rk5eLrjbvw1YYdSM3IbnZbpVKBuVPH4skVCzB9wogWE5wycguxbudJZOQ2/cW/iYkJ/H294OvtafhxcXJsMgGxtlaFjOwcpKZl4tTZCzh99jyKioWdILVaLdLT05GTk4MDh6Ph4+GKEH8fKBRyWFlaYs3yhXDnReNEjZLJZHCwt4ODvR3CQwLh6GAnSG60trKUMDppWFiYY+2y+Vj/0x6kpmcZxmtrVVi3eSdWLJyNAD/vZo7QMo1Gg6rqGpw8ewmlZeWCddMnjoGVlSWAtlWWJSKi7ukP//wQn6/bLBizsbbCvg2fIjjAV6KoiIiIiIiIiIh6j8qqamzeE40vf9yF05eut7j9Y8tm42+vPwd3V6dmt7udlIHth88jPaeg1bHI5XKYmprARKmEXg+oNWrY2dmhT58+KCgoQEZGBjQaYQFZjUaDO/EJ+DA9AzsPHMeIwf2weO40eLg6t/q8RL2BUqmEu5sL3Ov9blRX1yA3vwB5BUV1/+YXITs3X5BoZ2Fu3tXhSkYul2PBzMlQKpS4fD3WMK7X67Fz/1FoNBoMG9SvQ895PfYOklLTBWPDBvaDl4cbOCdKRGT8qqtrMP/xX+B6XIJgfGBUGLZ99e9mi4UQERERERFR98XkRupwiSkZePXNd7A3+jR0Ol2z24YE+OLJlfOxdsmsBl/8N0ar1eHA6RjsO3UNOl3jXzy7uThj3KhhGD6kPywtLFodt5mZKYL8fRHk74tJ40ZCpVLhp517sXXHHqRnZEH70G1Rq9V1tzU1Ezl5hegXGQwA2LJzH55/YlWv6j5H1F6VVcKqwFaWrf997UnMzEyxavFsbNi2XzDRplKp8cPW3XhkwUwEB/oZxu8nK1ZWVaOysurB/6tr7v2/CpWV1aiqrkZlVTVqVapGzxvg643+UeGdfvuIiKhrfPDFD/jrfz4VjJmammDHdx9gYN8IiaIiIiIiIiIiIur59Ho9rty4g89/3IkfdxxEeUVVs9vb29pg1aLpeGblfPQND2p225paNbYdvYxTVxOa3c7SwgK+3h7w9fGCr1dd0VcnR3vIZDLBdjqdDjl5BUjLyMLdpBQcP3UGN2PjGiQ5VldX486dO8jOzsbuA9HoExoAXy93qFRquLk6Y8Hs6bBlBzSiBiwszOHv6w1/3wfFS9dv3YX4xBTDcm8r+CqTyTBn2niYmChx7nKMYN3ewyehVmswevigDjlXVVU1Dhw9JRiztbHGpLEjOuT4REQkLY1GgxUvvIGT568KxoP9fbD3+/dha2MtUWREREREREQkFpMbqUPp9XrMffQV3L6b0uQ25uZmWDJrEp5cuQDjRgxCvTm1JuUWluKrn4422q1RJpNhQFQExo0ahtDggAYTde1hamqKR5YswIwpE7H3wGEciD6O1LRMVFYJJyQrq2tw9vJNBPh6Ilyvx6WrNzB8yADR5yfqaSorq1BTWwtHh7rJ9IbJjb1rIu9hpqamWLl4Njbt2C+Y3NRoNPhx2154uLk8SFasbTxZsdUvpgCUCiXmTJvQIa+VREQkvR9/2ouXf/t3wZhcLsf6T97GhNHDJIqKiIiIiIiIiKhn0+l0+Grjbnz03VZci20++RAAxo8YiCcfmYvFsybCwtysxe1vJ2fj+71nUVxW2eh6pUKBQQOiMG7kUAT4+bTqO3+5XA5Pd1d4urtixJABWL1sAfLzC/D1Dxtx7MQZ5BcK52HLysoAACfOFsLe1gb9+oSisqoKG7buxNOPreQ8A1ErNJgTtep9BV9lMhmmTxwNE6USJ89fEaw7dPws1BoNxo8a2uhrikajQWVV9b0ir7UPir0axh4Ufq2orILqXqHq+2ZPHc8uXkREPYBer8ezv/4bdh48IRh3d3XCgR8/hJtL853QiYiIiIiIqHtjciN1KJVK3WRiY//IUDy1agFWLpgBB3vbh9Y03oHxYWnZBfhw/QFUVtc2WOfh5oq1jyyEn49XO6Nunr29HVYuX4wxo4Zj74HDOH3uEuITkxt0pUxOy0JVVQ3MzczQr084LCzMOyUeImOh0+mQlZOH+MQUJCSlIDs3HwDg4eaCiWOGo6JSmCjc26qU1qdUKrF8wUxs3X0Qt+4kGsa1Wi0ysnI69FzjRg6Bk6NDhx6TiIikcSD6FB596Y0G4x+//Qcsmj1VgoiIiIiIiIiIiHqHN/75Cd7++Ptmt3F1dsCjS2bhyUfmIjTQt1XHrVWp8VN0090anRzsMXbUUIwcOgg21lZtjrs+FxdnvP7Kz7B0wRxs3rYLl69eR3pWNtRqYTfHkrJynDp/BaGBftDr9bh+Mw79+0aKPj9RT1d/TrS3FnyVyWSYPG4ETExNEH3yvGDdsdMXkV9YDFMTE0PB1/vJi4LCr21MqI4MDUZYcGBHhE9ERBJ74x//w9cbdgrG7Gytsf+HDxHg2znXDBIREREZE5YgIyJj1z2SG2Xyup/uQlQsLSfqNU0hYl+x5+4YZubmcHV2RF7Bg6qes6eMwZuvPYfB/SIar96p1zUce8jdtBx8vOEgalTCCntyuRxTJ47BrKkTYKLs/Keyr483nn3yUcyZOQ3xd5OwP/oEcvMKoVKpcPfuXQBAbkERTpy/iqjI05g3c0qzx9Nr1c2ub5ZO2/59xRJVgVXkRydRv5sSfmzrRVVra2pqcTc5FQlJqUhITEFVdc1Da+vuh+zcAqzfugdyuQwPPy5WVpYP7ispX87EPFxi4pbJoFAqsWTeDOzYdwQxsXdEHKyxw8tgY22FvhEhGDNiEATBtvA63CIx++tF3Glyke+bIh8vyYg5dy96Peo2JL3PxTxXRJ5azO+2CGIrxetFxS32Tmv7/ucvx2DJ4y9DoxFeaPaXN17G02uXte4gcpGPlUSPdZ1e+JpmrK8pUhJ9n0l0u435sTbazxtG+vnOaBnvbZZ18PeGMpnY7wOJiIiISCq7D59udFwmk2HGhBF4asU8zJk8GiYmrZ/HrKquxUebopGcVdBgnZmpKRbMnoqxI4dCLu/4ufUAfz/88uUXcDXmBg4eOYar12ORnJou2Ean0+P23RSUV1TB2soKEWHBMDVlRzSi0rJyJCSlID4xBWkZ2bC0MMfIoQMxqF8kKiuFnRt7e8HXcSOHwESpxIGjwtfQ2Nt3O/Q8ZqammDllbIcek4iIpPHuJ9/hnx9+IxgzNzfDzm/+g36RIdIERURERERERB2qeyQ3Uo8SFR6E6FMPkhuHDeyLIf3bV7UzOTMP//vxAFT1KoN6uLlg7SOLOq1bY1NkMhm8vTzh7eWJCWNH4cjxM9h1IBqFhYUoLi4GABQWl+KTbzdi2OD+cHd16dL4iLqaXq9HfkGRYbIuPTMbOl3rkhzqb2dladEZIRoduVyOBbOmwMREiUvXYpvcTiaTwcrSApYW5rCytHzo/xawsrJ88H/Luv9bWJiLTvohIqLuIy4+EXNWPovKKmHV758/swb/9/IzEkVFRERERERERM3S6zu+UJBeREFMUYXrxBbNExO3iPtQ7N3/UHEKZwdbwSp3F0c8t3oBHl86Cz6ebg9WtLJoaWlFNf636Sgy80sarAsJ8seaZQvg7OTYrrBbSy6XY/DA/ugTEYYbsXG4ces2jp++gIrKKmi1WmRmZgIAMnPycPzsJQzsF4UpE8c0f1Cdpvn1zRHxPBNXyKy3MtZCaF0ft06nQ3pmNhKSUpCQlIrc/ELB+praWuw5dAynzl+CRiv8HbCyNAfQEe8HYl4LpTw3MHJIfygVcuw5fKJTTmtubopFs6fAxspC+H4j6v1DxPuelHO0Ur4WGuvrsJi4JZ2PF1n4QFToUhYPNVJiniuiXsukfK9v377rNu/GL//4jmBMoVBgw0d/xdhh/Vv3+izqbw+RDQiM9bVQjN54m8WSSfg6qhfx/iG25o6YrxXEFMQX21hEzLlFNV0Q+T2MmGKbYn+vRT1NJXrP7K14nxERkYSY3EgdLjI0CNGnLhqWb91JbNdxsvKK8FEjiY1hIYF49rEVMDczExWnWPc7R/r7euM/H32JS5cvGybJ8gtL8Lu/f4BP//0WFIpu1JWUqANoNBokp2YgPjEFCUkpKCkt75DjWvXyKqUPk8lkmD11AsKCApCTVwALC3MmKxIRkUF6ZjZmLHsKhUUlgvGVi2bj3T/9mu8PRERERERERERdIDI0ACcuxBiWJ40ajD+8/Hi7jlVRXYv3N0Qjp7BUMN7Z3RqbYm5ujqGDB2Lo4IFY/cgS7Np/BEeOn0F5eTnKysoAALn5hXj/8+8weEAUHBzsuyw2IqlUVVXjbnIa4hOTkZiSjuqamhb3KS2raDDGgq91hg7sCxMTE+zYH92qZGS5XA5Li4eLvVrA0sLi3vypsCCsvZ0NlEpeDkVEZOz2HD6BJ159s8H4Z2+/gXnTxkkQEREREREREXUWfptHHS4qPEiwfLMdyY3VtSp8vPEQqmpUgvF+fcLx5OqlMDExERVjRwoJ8sdvXn0er73xJ6SkphrGY27G4csft+GZ1YsljI6oY+UXFOHHn3ajqLi05Y3vcXF2REigH0KD/FFaVoFjp8+juKSswXZ2NtYdGarRk8lkCAnyR0iQv9ShEBFRN1JYVIwZy55Cema2YHz6xDH46v2/dulFbkREREREREREvVlUWKBg+eadpHYdp6ZWjY82H22Q2Ghna4OXnnkUnu6u7Y6xI5iamGDx3BkICw7Efz/+CtdiHiR0Zufm4w//fB///dvvIZez4Bb1LHq9Hjm5+UhISkVCUioysnNEdwQ1NTGBqalpB0Vo/AZEhcPOxhqXr98CgIeSFO8nLD5IXjQ3N4OM338TEfUaZy5ew7KnX4dWK+yw9o83XsTjy+dKFBURERERERF1FiY3UoeLCgsWLN9JTIVKpYapaesTErcdvoCiUmEVw8iwYDy1Zlm3rLDn6+2Jv775azz1wi8EFRp/2LwTU8aNQKCvl4TREXWcsxevtpjYqFQq4O/jhdDgAIQE+sHB3k6wPioiBNduxOH4mYsoK6/7Pfdwc4GHxJPzRERE3V1lZRXmrHwOcfHC4iHDB/fDlq/e40UxRERERERERERdKCo0QLB8OykNGo2mTXOZer0eX+86jdTsIsG4i5MjXnrmUTg7OXRIrB0hKiIUr7/8HH71uz8jMyvLMH7u4lV8t3E7HluxUMLoiDpWanomduw7gqKS1hd8tTA3Q0igH3y9PXE99g7S6hWoA9i1sTEBft4I8POWOgwiIupGbt6+izlrXmrQJfkXz67Cr55fI1FURERERN2XTFb309vxPiAybt0vS4yMXp96VUo1Gg3ik1IRFR7cxB5Ct5MycfrqHcFYgJ83nnn0kW6Z2HhfaFAAXv7ZU/jnu/8zVGysqqrCv/73Nd7/y29gYtL5set0OnbroU4OKCfAAAEAAElEQVRVWFzS6LidrY2hO2OAn3ez3VUVCgUGD4hC/6hwJCSlorq6Bn0jQyHjp0oiIqImqdVqLH3iZZy/HCMYjwgNwu4fPoGVlaVEkRERERERERER9U596iU3qlRq3E3JRHiwX6uPcfZGEm4mZgnGHO3t8PLzj8OxXvHI7iAkyB9/+t2v8NzLr0OtVhvGv9u4DRPGDIe/j6eE0RF1DL1ej5/2HEJpWXmL27q7OhvmSL083Axz9YP790FichqiT51HVk6eYfvIsKBOi5uIiKgnSE3PwowVz6OkVPg+vHbJLLz925d4bREREREREVEP1X0zxcho2dnawNvDDRnZuYax2DuJrUpurK5V4fvdJwVjZqameHzlUqPoRLNwzkwcOXoKF69cM4zF3LiFz7//CSsWToe9nW2nfMmSk1eAwyfOIjktE27OTpg+cTT8OHlIneB+4u59fSNDMWb4YLi6OLX5ua1UKhERygk8IiKiluh0Ojzx8zewP1r4Odnb0x37N30BJ0d7aQIjIiIiIiIiIurFnBzs4O7iiJz8B10Xb8YntTq5sbisEluPXBGMWVtZ4aVnH+2WiY339esTjuefehQffPKlYd6ovLwc73z0Fd79829g0o2L1RK1RmVlVZOJjSZKEwT6eyM0yB8hgf6wtbFqdDuZTIbgQD8EBfjizt1kJCanwc7WBsMH9+vM0ImIiIxafkERpq94Hlk5+YLx2VPG4vN//Y4F/4mIiIiIiHowzixQp4gKDxIkN968k4jlrdhv2+ELKC6rFIwtmD0Nzk4OHRxh55DL5Xjlhafw/Ku/QVl5BYC6Ljtfr9+M+IQ7cHF0gLOTI1ycHeFkbwsXZwe4ODnC0cG+XV/AlJVXIPrkecTcumOYPMzKzcM3G7djxOD+mDRmWLMd9Ijaqn5yo7+PF9xcnSWKhoiIqOfT6/X4xe//gR+27BKMOzrY4cDmL+Hj5QHodRJFR0RERERERETUu0WFBQqSG2PjU7BkVsv76fV6/LD/AmpUasH4muUL4ObS/eddViyZj1NnzuFKzE3D2JVrN7BpxwGsXDSr0zrqVFfX4MyFy0hMToOHuysmjB4OGxvrTjkX9V566BuMDRkQhfDgQPj7ekEpSOBtuO3DZDIZwkMCER4S2MFREhER9SzlFZWYvfpFxCemCsZHDe2PjZ++DRMThUSRERERERERUVdgciN1ij5hQdh/9IxhOfZ2Yov7pOcU4vTVO4Kx0KAAjB05pMPj60yhIUFYMGcG1m3YKkgEi72ThNFD+yM7Nw/ZuXnQ67SGdQqFAk6O9nBxcqz7cXa8l/RoV29ypI5KpcaZi1dx+sJVqDWaBuv1ej3OXrqGhKRULJg5Cd6e7p1zY6nXqZfb2GmT00RERFTnH//9DO9/9p1gzNLSAnt+/IwdkImIiIiIiIiIJNYnNACHT10yLMfGJ7Vqv7M3khCXnC0YGzFkAPpGhnVofJ1FqVTixWcfxyu/ftNQ8FWlUuG9j77AtWsxCA3yr5vvdHaCi6M9XJydYG9n2+55Ja1Wi4tXruP4mQuorq4BAGTl5OHWnbuYOWU8+kaGcc6KOkz9+VAAmDJuFMzNzbo+GCIiol6gtlaFxU/+ApdibgnG+4QFYdd3H8DS0gLQqiSKjoiIiIiIiLoCkxupw+n1ely8FisYyy8sbnG/E5eEX1CYmZpi9bIF7epoKLUVSxbg0pUY3LqTYBgrKatASVk57G1tGmyv1WqRl1+IvPxCwbhcLoejg50h6dHZyQFqtQbHTp1BeUVlg+PUV1BUjC/X/4TRQwdiwuihjSZKErVF/c6NnCgmIiLqPF+s24zf/vU/gjGlUomtX7+P4YP7SxQVERERERERERHd1yc0QLB8805yi/tU1ajwU/QVwZidrQ2WzJ/ZobF1tqjICMyaNgkbf9olmD+6cuMWLC3MkJmdUzeg1wEATJQmcHZygIuzE5wd6/51cXaEo4N9k/PBer0edxKScOjYKRQWlTRYX11dg592HUDcnbuYM30SrKwsO/x2Uu9Tfz4U4JwoERFRZ9FqtXjs5d/j8InzgnFfLw/s//EjONjbShQZERERERERdSVmOlGH27jjIE6cE07IzZ4yttl9qqprcfGmsLvjlAmj4ezk0OHxdQVnZyf87a3/wy/e+BPy8gtQU1MDjUaD1Iwc2Ec2TG5sik6nQ0FhMQoKixGHh+4fvbbBtqYmJogMDcLN23eh0T7o5qjX63HqwhXE3+vi6OnuIuq2Ue/G5EYiIqKusW3PITz3yzcbjH/7v39g+qTmP1sTEREREREREVHXyC8sESynZua0uM+5G0morlULxlYumQdLC4uODK3TyWQyrFi6EFev38SdhAcdK8vKK1FUUgYnBzvB9mqNGtm5ecjOzROMKxQKODk43Ov06AgXp7qkR7Vag0NHjyMlLbPFWOLiE5GakYnZUyeiT0Rox9xA6rUaT26UIBAiIqIeTq/X4+Xfv42NOw4Ixp0dHXBgw8fw8nCTKDIiIiIi4yK/99Pb8T4gMm5MbqQOVVlVjV/9Wdhdxt/HEy8/taLZ/c5dT4Ba8yBhTy6XY8yIIZ0SY1fx8vTAY6sfwa79R6DX66HRaKBWqTBp3ChUVFQiLz8P+QXFqKhsuQNjc2QyGQb1jcTEMcNgbWWJ0cMGYvu+aGTm5Aq2yysoxBc/bMXY4YMwbuRgKBQKUeclApjcSERE1BluxsVj5bOvQafTCcbf++sbWLFojkRRERERERERERHRw7JyC/D3j9YJxob1j2h2H51ej5NXEwRjA/pGom9kWIfH1xU8Pdzxf6+9hP/74z+Ql1cArbZuvjc1I6tBcmNTtFot8goKkFdQ0HBlI0lmAODm6ozcPOH2VVU12LxjH+LiEzFr6gRYWJi37cYQ3cPOjURERF3jz//5DB99vVEwZm1lib0/fIiwYH9pgiIiIiIiIiJJMLmxw4n5UrvxyRnjOHedv73/JTKzhdU2333rtWYnj3R6PU5ejhOMDegbCTvb1nc47K5GDRuEvYeOQavVwsTEBCYmJlBp9Zg5ZSygravIWlVVjYKiYuQXFiO/sAj5BXX/lpVXNHPkusc6OMAX0yaMgquLk2GNi4sTnly9GKcvXMWx0xeg1T64KF6n0+P42cu4k5iKhbMmw83VuR23SsTzTPSkj5TnFqPnTHY1PofcCbdPyrtMzMuhqOdZx7wOt4/IO1wmIlla0l9NCU8u5twyMfVlxN1mcZP3Rnp/S8pY4xbLSD9Pi364Hvxuf/nDT6itVQnWPrFqCX7+7GNiT9LxZBK+fzVxcVsrdxaxL1/P2s6Y7zOJYuffTtTDSXlRaGMXqRIRERERtcf//fMTVFRWC8b++X/PN7tPfGou8orLBWMTx4zo8Ni6Up+IcLz8wjPYsmMf1Go1qqurUVtbi/CwEFRVViI/vwDVNTUdci4vDzdMnzwOvt6euHU7AbsPRqOqSnjsm3HxSEnLwJzpkxAWEtgh56XehcmNREREne/bTTvx1r8+FoyZmCjx01fvYsiAPhJFRURERERERFJhciN1mMSUdLzzibA66dRxIzB/xoRm94tPyUZeUZlgbNyooR0dniTsbG0wsG8kLl27YRg7eSEGk0YNMlyyaWlpAV9LC/h6ewr2rampvZf0+CDhMb+wGFXV1XB3dca4EYMRHOjX6HnlcjnGjhiM0EA/bNt7BDn1Kpfm5BXgs3WbMX7UUIwZPghyORsxU+vUn8zjRB4REVHH8/FybzC2ecc+PL5yMUYPHyxBRERERERERETUacR+zy6q9o+u5W2a3Fcr4sQAdCLODTH7inRvnuTM5ZtY99MBwarHlszAsH6hgE7T+L4yGU5cuSMY8nBzbXK+z5iMHDIQO/cdgUwmg6mpKQDAztYejyyYBb1Oi8rKqntznkXILyxEQWEx8guKUFFZ2exx9fee4Ha2NpgyfjSiIkIhk8mg1+sRERYMX29P7D54FHHxdwX7lVdW4sefdqF/nwjMmDyul3VxFPGiILoYjETFZDo4bn0jr411L9WNnEeyAmwSn1sMUe89HRdGl5Ky0JKkRZ6M9QETQXQNTgkLgBppTUijZawF2EQ9R4XXpv3h7Y8abPHtB3/FlPGjmji3iGvbFKbt31eranmb5uib+NugVfu2/28uFvkzLjIxL6R6kdd9itld7J/mUp1bLqKIPwDoRHwfIubcot9vJXyzF/OaJOnnDH7IISIi6kpMbqQO84s334FKpTYsK5VKvPfnX7aY/BR7N02w7OHmipBA/84IURLjRg0VJDfmF5Ygr6AYbo7Nd6Y0NzeDt6c7vD0bXtze2j803Fyd8fSaJTh57jJOnL0Ene7BflqtDtEnz+PO3WTMnDy2ifMQCTG5kYiIqPP97MnV2HPwGI6eOmcYK6+oxIxlT2Ln959g4ljjruZPRERERERERGTMtFotfv7H9wVjttZW+Puvnm52v+LyKly/myUYGzdqWI+Ya7G0tMCQgX1x9sIVw9jJi9cwbfwIKOQyWFtbwdraCgF+PoL9qqqqUVB0L+mxoOheAmQhysorAADmZmYYM2IIhg/uDxMTkwbntbKyxLIFs3AzLh57Dx1r0CEyJjYOSalpmDdjCkKC/Dv+hlOPxFwAIiKizqdUNEzwOXj0DJbOnQZFI+uIiIiIiIioZ2NyI3WIfdGnsevQCcHYS08+goiQwBb3TcsuFCwP7BfZIybx7gsK8IOtjbVhEg4A0rJyW0xu7CgKhQITRg9DWHAAtu05jLyCIsH6zOw8fPH9VvQJD8aUcSPgYG/XJXGRcdKDyY1ERESdzczMFLt//AyLHv0ZDkSfNIxXVlZh9oqnse27jzB90lgJIyQiIiIiIiIi6r2+2rwPV24mCMbefPlRuLk4NrvfjbuZgiKSZmamGDa4X6fEKIXxo4YJkhtLSsuRlpmNAB+PJvextLSAr6UXfL29BOM1NbWorqmBrY0V5PLm23nIZDL0jQyDv48Xdh2IRnxismB9eUUlftiyAwP7RmLK+NGwsrJsx62j3o5zokRERB3rP39+HYuf+AW02gcd0L7ZuAO1KhW+/eCvUCp5WSsREREREVFvIrInORGgUqnx6h/+LRhzc3HCH15tvjopAOj0eqTnCJMb/Xy8mtjaOMlkMvh6ewrG0rLyujwODzcXPLN2KcaOGNzo5Evs7bv48Mv1OBB9CtXVNY0cgYidG4mIiLqKhYU5tq/7GHOnTxKM19TUYv7q57Brf7REkRERERERERER9V7FpeX47b+/EIxFBPvhxbULW9w3NadYsNwvMhwW5uYdGp+UfL094ebqLBhLzcxp17HMzc3gYG/XYmLjw2xsrLFi8VwsmDUVZqamDdZfvXEL73/2LU6duwS1Wt2uuKh3qD8fCnBOlIiIqKPNmz4RGz59u0ES44/b9mHFs69DpeLnNSIiIqLWksn4c/+HiIwXkxtJtP9+sR7xSamCsb+/8RLsbFvuTJhXWIrael9G1E8E7Anq36b0zFxJ4lAqlZg8bgSeXLUIzk72DdZrtTqcvRSD/36+DmcuXIVGo2n2eFVV1bidkIS0jKxGJ3mo52FyIxERUdcxMzPF5q/fx5J5MwTjKpUaix97EVt3HZAoMiIiIiIiIiKi3unN975GQVGpYOy9378IE5OWO8uk5xQJlv18e1bBVwDw9/EWLKe1M7mxvWQyGQb0jcQLT65GkL9vg/W1KhUOHz+ND79Yh5ibcZzfpEYxuZGIiKhrLJ4zFVu/ehempiaC8a17DmPJk6+ipqZWosiIiIiIiIioq7U8y0LUjOzcfPz5P58LxoYPisLapXNatX9adoFg2c7WplVJkcamQXJjdh50Oj3kcmkmQbw93fHco8tx8epNHD97ETU1KsH6mhoVDh47gwtXb2DKuJHoEx5smLBRq9WIT0zB9VvxuJucBq1WCwAICw7AvOkTYWVl2eW3h7pOw+RGiQIhIiLqJUxNTbH+s3dhamKC9Vt3GcY1Gg0eeeoVjBw6AG7OTnBxcYKbixPcXJzh5uIEV+d7y65OsLay4sU3REREREREREQi3bidhI+/3yEYWzhtLKaOHdLiviq1BtkFZYIxvx5a8PX85WuG5a5ObrzPztYGq5ctwOVrN3Hw6Emo6nVqLC0rx7Y9B3Hu0jVMmzgGAX4+zR5Pp9OhqLgEVpaWsLDoOd02qXEs9kpERNR15k6bgJ3ffYAFj70sSGbcfegEFjz2Mn766j+wtLSQMEIiIiIiIiLqCkxuJFF+89f3UVFZJRh7/y+/hlzeuqag6dn5guWe2LURaHi7ampVyC8qgZuzg0QR1XVxHDl0AAZEhePE2Uu4cPUGtFqdYJuS0nJs2XUQZy5eRXhwAIpKyhCXkITaWlWD4925m4yPs3OxYOZkBAf6ddXNoC5Wv1ApJ/OIiIg6n1KpxLcfvQ1TM1N8s36rYVyr1eLUucst7m9hYQ5XZ8cHiY8uzveWnQw/99c72Nu2+rM8EREREREREVFvodfr8fKfPhTMpZmbmeKd373Qqv0z80uhe2iSRSaTwdvTo8PjlJqvt/A2ZecVQqVSN+jG0xVkMhmGDOyLkCB/RJ84i5jYuAbbZOfm4dsNPyE0KABTJ4yGi7OTYZ1er0dObj6u37qNm3HxKK+ohFKhxLhRQzFmxBB+h9aDsdgrERFR15o2YRT2/vAR5q55EZVV1Ybxg8fOYPyCxzF6+EC4OdnDzcURbs5OcHV2qJvjdHaEmZmphJETERERERFRR2FyI7XbmYsxWLdlj2Ds8UfmY+iAPq0+RnFZpWDZ0921Q2LrbuxsbWBpYYGq6gdfwJSUVUia3HifhYU5pk8ag2GD+uLIyXO4GXe3wTa37iTix5/2YsSQ/jAzbfpLoYrKKny/ZRdGDOmPKeNGQqnkS0xPJwNn84iIiLqCQqHAF+/9FeZmZvjk6/Vt2re6ugap6VlITc9qcVulUgkXJwdBF0gXQyKk84NESFdnuDg5QKlUtPcmEREREREREREZjS37TuDYuRjB2K+eeQT+3u6t2j8tp0iw7O7q3CMvxPb29IBMJjMkh+n1emTk5CHQ10uymOxsbbBwzjQMH9wfh46dRnJaeoNt4hOTkZCUgsH9o9A/KgLJqem4HnsbBUXFgu00Wg2iT55FYnIaFs6ZBns72666GdSF2LmRiIio600YPRT7N3yCWStfQHnFg+sJL1+/hcvXbzW5n52tNVydHO8lPjrC1eVe8uP9+U1nR7g62sDN2QHWVpZ8XyciIiIiIuqmmHlE7aLVavHy798WjNnaWOPvb7zUpuOoNVrBspmZmejYuiOZTAYzM1NBcqNao5EwooYc7O2wZO50jBjcHwePnUFaRrZhnZ2tDQL9vKFSqQE9WpxsPXcpBsmpmVg8ZypcXZya3ZaMS/3JPD30TWxJREREHU0ul+N/b78JaytL/PvDLzrlHBqNBtm5+cjOzW9xW5lMBidHe0EXyLoJQ6d6XSHr/jU375mf9YmIiIiIiIioZ6usqsYv//qJYMzX0w2/fm5Fq49RUCos+Orl2bqkSGNjZmYKV2cn5OYXGMYKikolTW68z9PDDWsfWYiExBQcOnYK+YXChFO9Xo/9R07gvU++xoghA5ot4pqakYlPvl6POdMnISoitLNDpy5Wf/aTxV6JiIi6xuhhA3F48+eY/sizKCktb9U+pWUVKC2rQEJyWovbWpibwc3FEa5OdcmPdR0gHywbEiSdHeBob8tESCIiIjIasns/vR3vAyLjxuRGapevNuzA5etxgrG3fvksXJ0d23Sc+gl+JiY99ylZ/7ap1NomtpSWt6c7Hl+xELcTknD4xDlciYlFUUkpLC0skJiSjgA/b/j7eCHA1xv9IkPh6+2B/dGnEJ+YIjhObn4BPlu3GVPHj8SwQf34hU8PUT+5cd/hE7CzsYFHD+26SkRE1N3IZDK8/dbrePSRhYi9nYDc/ALk5hUgr6AQefmFyM0vRF5B3b9VVdUtH1AEvV6PgsJiFBQW49adxBa3Hz1sIDZ+8W6P7dZORERERERERD3TPz/dgPTsPMHYv994HpYW5q0+Rv2Cr5YWFh0SW3dkUe9+Uau7T8FXmUyG0OAABAf64UpMLI6dPo+KygeJp/Z2tnB3dQEgg1ajhUKpaPJYNbW12LJzHxISUzBr6vgeW8S3N6o/H0pERERdZ+jAKERv/RLTlz+L/MLilndog+qaWqSkZyMlPbvFbZVKBVydHODqZA835wfJkHXLjvfGHODq7AAXR7tmC2MQERERERFRy/hXFbVZcUkZfvv3DwVjkaGB+Nljy9p8rPrzAj05/a1+cl93nhSRyWSICA1CaJA/PvjiB8TevgtbG2tUVFYhyN8HLz+zFjbWVobtVyyajYtXb+DgsTPQPJSwqtFosO/ISdxNTsP8GZNgbWMtxc2hDuTi5IiS0jLDcmlZOb5avxULZk1Bn/AQCSMjIiLqXfqEhzx479XrGt2moqJSkOyYm19Qt5z/IBEyN78AeQVFgvf3znL6wlX8/h8f4Mv3/tzp5yIiIiIiIiIi6gjJ6dn416cbBWMTRw7E4pnj2nSc+smNvanga/1it92BXC7HkIF90TcyFGcuXMGZC1eRmp6BsvJKKBQynDp3CWHBAfBwd4W9nS36RoTBw80FB4+davA9WkxsHNIys7BozjT4eHlKdIuoI9Wfx9dotTh07DQmjxsJuVwuUVRERES9x4CocMQc3Yotuw4hLTMb+QVFhnnO3IIi5BUUQaPp3KYCGo0WWbkFyMotaHFbmUwGJwdbBPl64vXnVmLh9LGdGhsREREREVFP1HNnTcSQrMOchKl9bcize+fT71FQVCIYe++Pr8FEqWjywuqmmCiFX/539hcPUqpfldRU1KSliOdKG57fCqUc40YORUlZBQBg8IAo2NnawqZekqJMJsOwwf3h7+uNrbsPIje/ULA+ITkNH3+7EfNnTkZoUECXxN7IzhKdt2eZOXU88jcWoaS03DCm1miweed+5BUUYcKY4R3TpVPK3F9R8YsJXOT9JiZh2mif42KfKFK+Log5d/snzsX/fkr4XDHa56mRvv9I+Osliuj3Dwlfx0Xt3vjrgrWNDaxtbBAU6N/MvnW3ubZWVZcEmVeX7GiYIMwrQL4hObIuUTK/oKjdhTri4hMBWdMV79tE1n2LhTSrGxc5aZakr8NG+lraG+M2Yh3ydwwREREREfU4r/31Y9Sq1IZlhUKO//7hpTb/DaHVCb8PkIv4nre7U9RL/tLq2jZ/3JXMzMwwcexIDO4fhb+8+z+oNRpYWVogr6AIHm6ueGLVUvh4eRge70B/X+w7fBwxsXGC4xSXlOLr9VsxbuRQjBs1jAlwRs62kYK9py9cRX5hMRbPmQYzM1MJoiIiIupd3F2d8eKTKx4MaFWG/+r1ehSXlBkSHXPzi5BbUIjc/CLkFRbfK/JahNyCIuTmF6K6prZTY9Xr9SgoKkVBUSmW/exNpJzaCC93l049JxERERERUU/D5EZqsy27DgmWF82ahCnjhrfrWCZK4VNQpVY3saXxU9e7bSbKDrqou5O5uwm/bCkqKYVKpYKpacNJG1cXJzy9ZimOnDiHs5euCdZVVlVj/dbdGDawH6ZOGAUTE5PODJs6iaODPZ5esxybtu9FakaWYN3xMxeQV1CIhbOnNvr8ICIiou7LzMwUPl4e8PHyaHFbrVaLgsLiuq6P+cKukPn1lvMKigSfg6urO3fykIiIiIiIiIioo5y8cB3bD54WjL2wZgGiwtpeyLNBwVdt9+tm2FHqd2qsPx/cHdna2mDu9Mk4fLzu8R451B4+3h7w9RZ2YjQ3N8PCOdMQHOiH3QeiUat6cJG9TqfDsdPnkZiShkVzpsPB3q5LbwN1HFsba4wfNRTHz1wUjMcnpuCL7zdjxaLZcHSwlyY4IiIigkwmg6ODHRwd7BAR0vJn84qyknvJjsXIKyxCbn5xXeJjQRHyC+7/v+7f0nsNANpLq9XhamwCkxuJiIiIiIjaqPvPJFC3kpCUivikVMHYr198ot3Hs7Y0FyznFRQ2saVxq6quRnlFpWDMytJComjaxsXJATKZzNCdR6/XI6+gCN6e7o1ur1QqMX3SGAQH+GLb3sOoqKwSrL9w9TqS0zKwZO50uLk6d3r81PGsrCyx9pGF2HvoGC7HxArWxcUnoqi4BCsWz4W9na1EERIREVFnUigUcHN1rvss10IXwi0792PZk68Ylqtrajo5OiIiIiIiIqJuTCbrhE7tIo7Xwt/1nUtMNz8xXfFaf971O6MFy86OdvjjK4+366z1i56q1T04ubHebTMxUQCQ8rnWOm4uzoYOjTKZDLl5BdDr9Y126ewbGQYfLw/8tPsA0uoVA03PzMYnX6/H7GkT0a9PeDsiEXlfSfl7Ldm5O/4+mzh6GGytrbDn0HHoHuo+ml9QhM+/24yl86Yj0N9H3LlF319SnlsiksZtpPeZlIz1eSaG2I95Ut5lYmIX9ViLvNEd/tm6lSR9DRfzOVrCzxk6kZ9/RdxuaytLWFtZIsjfu8Vta2tVyCssNnR+zMsvNCQ+5hUU1627t1xQVGq4nu5hVTVqQHbv879CzHO0/Y0DZGK7p4t4vPQ6kc0tjPX9Q8TrkV7E37gymdjHWsS+MpGPVff/WqCJc4toaiLqNVzMjQZE3XCZ2HOLYKyfjyRltIETEVEvx+RGapM9R04Jlt1dnTG4XwTa+wnS281JsFx/8qenSM/IFiwrFQp4uDhKFE3bmJiYwNnRHvmFxYaxnLyCJpMb7wsK8MXzjz2CnQeO4s7dZMG6/MIifLZuE6aOH4Xhg/s3OilI3ZtCocCc6ZPg5uKM/dEnoNM9eA3IzS/EZ99uwPKFs+Hn4yVhlERERCQ1C3NhMZPqGnZuJCIiIiIiIqLuT6/XY8/Rc4KxV59YAntb63Ydz9LMVLBcWFTcxJbGTa/Xo7CoRDBmYWYmTTBt5F6vKKtKrUZxSWmTHfrs7Wzx2IrFOHXuEo6dPi9IgKtVqfDT7gNISEzB7GkTYW5uHPcBCQ3u3wfOjvbYuH0/qqqrDePVNTX4fssuzJg0BkMHRnGum4iIqAcxMzOFj6cbfDzd6gZ02ia31Wq1KCgqxZRVryA2/sG1cZwPJSIioq4ml9X99Ha8D4iMm4TlFMgY7Tl0QrA8c9JoyOXtfxr5eQonifLyC3tkN5f6SZuebs5QKkVUcOlibi7Cxyk3r6BV+1lZWeKRhbMwZ9oEmCiFudRarRb7o0/ihy27UF5R0WGxUteRyWQYNrg/Vi+dD4t6k7JV1TX4buM2XL52U6LoiIiIqDuwsKif3NjzPusTERERERERUc9z43YSMrLzBWPzp45q9/E8XewEy2kZ2Y12eTF2RcWlqKyqEox5e7hIFE3b2NhYw9LCQjDW0pyoXC7HuFHD8MTKJXCwt2uw/kbcHXzyzfoeW+C3N/Dz8cIza5c2mC/X6XTYe/gEdh88Bq226aQHIiIi6rkUCgXcXBzhYGcjGK+q5nwoERERERFRWzG5kVqtrLwCJ85fEYzNmTpO1DE9XBygqJccWb/LYU+QWm/CytfLTaJI2qd+pdKcViY3AnUJcEMGROGZtcvg7tpw8vJucio+/noD7ialio6TpBHo74un1y6Hi5OwG6lWq8OuA9HYe+iYoFotERER9R71CyBUV7NSKRERERERERF1f7ujhV0bA3w8EBHs1+7j+bo5CJYrq6pQVFza7uN1V2mZwjlRSwtzODk0TPrrjmQyWYMEtpy8/Ca2FvL28sBzj63AgKjIButKSsvw9fotiD5xlklwRsrezhZPrlqEiNCgBusux9zCd5t2orKyqpE9iYiIqDdoMB9ay/lQIiIiIiKitmJyI7XaoRPnoFZrDMsmJkpMGTtc1DFNlAp4ugoTonpi5cr0+smNnsaV3Ojm4iRYzs0vbHM1WRdnRzy1eglGDR3YYF1VdTV+3LYHWTm5ouIk6Tg62OPJ1UsRGuTfYN2FK9exY+/hrg+KiIiIJFe/2n1VdbWorgR6vR7HTp/Hp99uwIUr11lAgYiIiIiIiIg6xd5j5wXLsyYOh0wma/fxnOysYGVuKhirnwjYE9Sf5/XxdBN1v3U1dzdhcmNufmGr9zUzM8OC2VOxdP5MmJsJL3DX6/U4cfYCvl6/BcUlPS+ptTcwNTXFsvkzMH7U0AbrUtOz8Pn3W1rs9ElEREQ9k0W9z37VNSqJIiEiIiIiIjJeTG6kVttz+JRgecLIIbCxthJ9XF8P4SRR7O140cfsTnLzCpBfWCQY8zG2zo1uwo6LKrW6XRNvSqUS0yaOwdplCxo8d7RaLbbvPQKNRtPE3tTdmZub4ZFFczB62KAG62JibyMrm8mrREREvY2FRcMLuVQqdbuOpVar8exrf8CkBY/i+V++hRHTl8M9cgzWvvA61m/djYLC4o4ImYiIiIiIiIh6uYKiUpy9ckswNmfSCFHHlMlk8HEXFhNNS88UdczuqP5t8vP2kCiS9mlv58aH9QkPxfNPrIK/j3eDdRlZOfh6/RZUVVW3O0aSjkwmw8Qxw7F03nSYmCgF60pKy/Hl+p+QlZMnUXREREQkFYt6RUyqa8R1btRoNDh29iqu3oxnoVciIiIiIuo1mNxIraLT6bD3yEnB2OwpYzvk2KH+noLlO3eT2zVR1F2dPHtRsGxlaQ6velU/uztrK0tYWpgLxtpSqbS+QH8fPP/YCoSHBArG8woKcez0hXYfl6Qnl8sxdeIYLJozDUqFQrDubnKqRFERERGRVCzMzRuMVdfUtPk4pWXlmLvyOXyxbrNgvKCwGN9v3oXVz/0KbhGjMXLGcvzxX/9jV0ciIiIiIiIiard9xy9Ar9cbli0tzDF+eH/Rx/X1cBQs37mbLPqY3YlKpUJyWoZgzNfLXaJo2sfdVVjwtaS0DDXtuDjdztYGax9ZiCnjR0MuF16SUVZegX2Hj4uKk6TVJzwET6xcDFsba8G4SqXG3sMnJIqKiIiIpGJhXr9zY/uTG5PSsjBw1lOYtOIVDJ7zNDyGLsTaV/+C9TsOoaCoRGSkRERERERE3ReTG6lVLsXcQl6BsPtgRyU39g/3h7Wl8KLnE2cuNrG1camtVeHsxauCsZGDoqCol/TV3clkMri71q9UWiDqmJaWFlg6b0aDScLTF64gIytH1LFJev36hGPIwL6CscTkNImiISIiIqk0mtxY3bbkxrSMLIydsxIHj51udju9Xo/zl6/jj29/aOjquOb51/HDll3s6khERERERERErbb36DnB8tQxg2BuZtrE1q0X5O0qWE5Jy0B6Zrbo43YXF6/eECQCymRAgK9nM3t0P85ODg2SEXPz2zcnKpfLMWbEEDy1ehmcHBwE627E3cGt2wntjpOk5+HmgmfWLIVPvQTejKxcVFRUShQVERERScGiXsOAqjbOhd53/uotjFz4PGLjHxRByS8swffbDmH1y3+B2+AFGLngefzxvW9w4VocC70SERGRgYw/hh8iMl5MbqRW2XNY2LUxPDgAQf4+HXJsE6USIweECcbOXbqK2lpVhxxfSpeu3RB0ppHJgDFDxVd2lYKbizC5sb0TeQ9TKBRYOGuKINlTr9dj255DUKvVoo9P0gry9xUsp2dlo7a2/dXJiIiIyPjU7/4NtK1a6ZWYWIycsRw344QXe9W/yKwxBYXF+GHLLqx5/nV2dSQiIiIiIiKiVlGrNdh/XFiEdfakER1y7IhAT9hZWwjGTpy50CHHlpper29wWyJDAuFgZytRRO2jVCrh4iTssJkrsuCrp4cbHluxqEERsN0Hj6KyskrUsUla1tZWeHT5fJjVS35OSs1oYg8iIiLqiep3bmxP5++t+45j4iMvI7+wpMlt9Ho9zl+7hT++9zVGLHgO7kMWsKsjERERERH1GExupFapn9w4a/KYDj3+mEERgmz5mppaXLx6vUPP0dUam8SLCAmAi5O9NAGJ1KBzY6745EYAcHN1xoRRwwRjhcUlOHLiXBN7kLHw8/GEQvHgbUan0yMlLVPCiIiIiKirmdebzAMgKP7RnN0Hj2L8vDXIzs0XjLs4O+LUnvVIvHQIH/7z95gzbQIsLS2aOEoddnUkIiIiIiIiotY4ffkmSsuFXddmTRjeIcdWyOUYMzBUMHbxynVUVVd3yPGl1FgXynEjBkkUjTj1C77m5OU3sWXr2dhYY+aU8YKxqupq7D54FHq9XvTxSTpKpRL+PsIOpYkp6RJFQ0RERFKon9xYXdP6hg56vR7vfL4Ry154EzVtbARRUFTKro5ERERERNRjMLmRWpSVk4crN+IEY3OmjuvQczg72KJPiLDL2/7Dx1t94XN3dCUmtuEk3jDj7NoIAG4uToLlkrLydlWaaszo4YPg5eEmGDt/JQap6UyEM2ampqbw9ao/mZcmUTREREQkBblc3qByeVV1y5/xP/zieyxY8zNUVgmr14eHBOLsvo0YMWQAAvy88cITK7Hzh49RcOcsDmz+Aq889yjCQwJbPH79ro4jprOrIxEREREREREBe46eFywPigqBp5tzE1u33aj+wZDLH5R8VanVOH8ppsOOL5UTZ4TdLh3tbdEnrOXvaLojdzcXwXJufscUfO0bGYaI0GDBWFz8Xdy4dadDjk/SCfL3ESwnpqQzaZWIiKgXsTAXzoVW17buejqNRoOf/f4/+NVfP2rw2WHq2CGI2f8VPvzzK5gzeSQsLcybOEqdBl0dhy3Cmlf/ih+2H0JBUWnbbhAREREREZEElFIHAACQyep+Ou6AHXisNuqBX1LvPXJKsGxna43RQ+sn6Ym43fce+7FD+uBmwoPEp6KSUmzbfRArl8xr/7ElUl5egY3bdgvGnOxt0Sc04MGAzLhyi12cnaBQKKDVPrjYO7egCH4+Xm04SuO/m3KFHAtmTcOn326ARqMBUPertH1fNJ5/fAVMTU0b3a9NOvQ1piuJjVva16SgAF8kp2UYlhOTW5ncaKyPl5R3t6T3mZgbbqSPtej7u/37y0SdW2Tcxvq7SW0n9rEW9Xoo3YupmN8v8ReriLjPRT1cnf9Zw8LcHLUPVRqtrlE1+VlYq9Xil3/4J/776TcN1k0YPRxbv/0QDvZ2dQMPPV7mlkpMnTQeUyeNx7t/BZJT07Hv8AnsP3Ic0SfPoaqq6Q4Ier0eF65cx4UrdZ0dnZ0cMH3iWMyYMg7TJ42Fs5Nji7exTfQSJk8a69+rkr7/iPndNNK4JSTuc44xM9bbbaSvKUREREREzdgTfU6wPHviiA49vr2NJfqH+uLq7VTD2OHjpzFy6ECY1+v4Yixy8vJx+doNwdjY4QMhlxvXXOh99Qu+5uUXQafTib49MpkMc6ZNRGp6pqBb595DxxDg6w0bG2tRxyfpBPkLizhXVFYhL78Qbq4dlxhNRERE3ZeFWf3OjS0nN5ZXVGHFS3/E3qPnGqx78pHZ+OjPv4CJiRJ9w4PwwpqFqKmpxcmL17Hv2HnsP3YetxObv/6qoKgUP+w4jB92HIZMJsOw/uGYMX44Zk4YhiF9w4z2szoREREREfVc3SO5kbq1PYdPCpanjx8FExOTDj9Pn2AfhPp7Ij4lyzB26twlDOrXB+GhQR1+vs6i1+ux4afdqKgUdpmZN22sUX8xoFAo4OzogNz8QsNYbn5hG5Mbm+bi7IhJY0fg4NEHybTFJaU4dOw0Zk+b2CHnoK4X5O+Lw8fPGJYLi0tQXFL6ICmBiIiIejxLC3OUlJYZlpvqzl5ZWYXVz72GHfuONFi3Ztl8fP7eX1td9CLAzwcvPLkKLzy5qm6y7+xF7DtyAvsPH8fthKRm963r6rgTP2zZCZlMhqED+2LGlPGYOXkchgzsC4VC0aoYiIiIiIiIiBrQ6zuh8IyY44kowCP2dojZv5PqgySmZjW4SHj2pHrJjXpt+0+gqPteY9yQPoLkxuKSUvy0+4BRFnzV6XT4bsM2aLQP7helQoFRg/sabZEld1dh50a1Ro2i4pI2FsBq/LZbWVlgzrQJ2LRjn2GsprYWO/cfxsol8+qK/oi+30Tsb7TnlvD1CHWdSu1tbVBS9uA70MSUtAaJso2cWNR5Jf0dk/LxEsNIX5eMVy8skNtriXn9l7AgpBii45boPtOJjFvMZ2Ex+wIi73MRrynylucFLS0tBctV1bUP7dfw8tzMnDzMffyXuBab0GDd3379PH79wpq6z4UPndvc2gJTJ47D1Inj8C6A5LRM7Dt6FvuPnkH06Uuoqm58/hW439UxDuevxeGP//0Gzo72mD5+BGZMHIXpE0bA2dG+kZ3af3/L1E0XnW0NvbZ1nS8bpdOIOrcooj5miHh+y4x57lrM65mI04q9hFfKcxsrUY1gJPxMaax/P4i62R38GcVY70MjJJMBcv4Zxd4dREaut35Uola4fP0WHnv599hdL7lx9tSxnXI+mUyGVXPHw9RE+Ef995u2N3kBdHd0JSYWV2/cEowNiAzB4L5hEkXUcdzrVZfMyc3v0OOPGDIAvt6egrGLV2+0vtsfdTvubi6wtDAXjCWlpEsUDRF1tfKKStxKSMbFmFs4e/kGTpy7ipPnr+HclZu4fP02ktIyoVKppQ6TiDqZRb3PAo19ts/JzcfE+asbTWx88/WX8M3/3m53N29zczNMnTgG7/7lDdw6dwCJV6Lxv3+9hTnTJ8LS0qLZfe93dfzT2x9g5PSl8IgYiTXPvoYdew9DpVI1uy8RERERERERGZc99bqmuDrZY0jf0A4/T4ifO0L9PARjp85dwu34xA4/V2c7cvwMUtIyBGPjRw6CjbWVRBGJZ2VlCWsrYfw5eQUddvzI8BD0jRA+rxKSUnGt3vwyGQ+ZTIYgfx/BWCLnQ4mIiHoNi3od2Ktrm07Oi7mVgBHznmqQ2GhmZoofP/wzfvOztXWJjS0I8PXCC48uwc5v3kXBjUM4sP4DvPL0CoQH+7e4b0FRCX7Yth9rfv4HuPWfjhFzHsdb73yG81duQqsVmYRKRERERETUTuzcSAJqtRo/7Y3GB1/+iDOXYhqsl8lkmDFxVKed39nBFgunjMDGfQ+69xWVlGLTtr1Y+8jCVv3xLqWi4hJs3LZbMGZlaY7l8yZ3+9hbw6rexd+l5eUdeny5XI4Fs6bg469/hFr9INllx77DeOGJVTCv92UQdX91k3m+uBEXbxhLTEnD4AFREkZFRJ2hqroGyWlZSMvMQVpWLtIyc1Bc2vL7hEwmg4erE3y93O/9uMHPy52d0Yh6EAvzesmN1cIJvdjbCZiz4hmkpmcKxk1MTPD5e3/B2uULOzSeAD8fPP/EKjz/hLiujk6ODnhk0RysXb4AQwb27RGf94mIiIiIiIh6o4rKanz300H867ONgvFZE4ZDLu/4WsEymQyr5ozFXz/7CSr1g84i32/egd+99jOjmQ/Lzs3HrgPRgjFXJwfMndI5hXK7krurM+4mVxqWc/MKEBXRcYmus6ZOQHJaBioqqwxj+4+cRKC/L+xsrDvsPNR1gvx9cPl6rGE5NT0barUaJiYmEkZFRF1BrdEgK68YFVXVUGu0UGu0kMlkUCoVMFEqYG9jBXcXByg64TMFEXUPDZIbaxpPbtx39CyWv/A7wWdAAHBysMP2L/6J0UP7t+v85uZmmDpuOKaOG45333wVyWmZ2H/sLPZFt66r44VrsbhwLRZ/+s8XcHa0x7RxwzF36hjMmzq2QRFbIiIiIiKizsLkRgIA5BcW4bPvf8LH325CVk7T3fjmTBkLFyfHTo1lzJBIXI1LQnxKlmHs/OVrsLOzwYJZUzv13GKUlVfg/U+/bfAFxLI5k2FrxNVJ71OpVLh287ZgzMHOrsPP4+hgj6kTRmHvoeOGsbLyCmzddQBzZ0yCLSf0jE5QgDC5MSklHTqdrlMuCCCirqXX65GcnoUT567iyo070LSjip9er0dWbgGycgtw7spNAICtjRVGD+mHMcMGwMHOpqPDJqIuVn9Cr6q62vD/IyfOYMljL6G0TJgMbW9ni5++/R8mjBneqbHd7+p4v7Njcmo69h85gf1HTuDIibOoqqpuct/ComL874t1+N8X6xARGoQ1yxdi1dJ58PHyaHIfIiIiIiIiIuo+ktKy8OF32/H15v0oLa9ssH72pBGddm5nBxssnDwUG/efNYwVFZdgy859WLV0frcvoqTWaLBu4zZoNA+SM2UyYM2SWTA1Nf5kLns7W8FyUUlJhx7fwsIc82ZMxvqtuwxjtSoVduw9jDXLuv/jTw0F+HpBJpNBr9cDADRaDdIysxHk7ytxZETUkTRaLTJyCpGWXYD07HykZRcgK78IOp2+2f1MlAp4uTnB18MZvp4u8PVwgYeLA6+ZIOohWpPc+On32/Di799p0BkxJMAHe759B8H1ukCLEeDrhefXLsHza5fUFXq9cA37jp7B/qNncftuSrP7FhSVYP32A1i//QBsbaywZPYkrF08C2OG9edrFhERERERdSomN/ZyKpUav3jr3/hy/TbU1qqa3M7MzBSrF8/G2797pdNjkstkWDV3PP76yWZBtdKD0SdhZmqCmVMmdHoMbVVeUYkPP/8OeQWFgvEBkSEY3DdMoqg61pXrt+5diP5gMm1IJ3XfGzqwH+LiE5GcmmEYS0hKwQefr8OooQMxevggmJqadsq5qeMF+gm/gKuprUVWdi68eeE/kdGqValwMSYOJ85dRUZ2Xocfv6y8EvuOnsX+Y+fQLyIY40cMRGigH+RyXtBBZIwadG6sqasO+s2PP+GZV38nuAgOAAL8vLH7x88QERrcZTE+OPeDro61tbU4caZ1XR3j4hPxxp//jd/+5R1MGjsSa5YvwKI502DdA4qcEBEREREREfUker0e0Weu4oNvt2HXkbOGRKT6HO1tMG3skE6NZczgCFyJS0FCarZh7MyFK3BydMDMKeM79dxiaLVafPPDFqSkZQjGJ40eiiA/b4mi6jgqlQq37twVjNl0wnc8ocEBGBAVgWs34wxjSanpOHryHMaPHgaFQtHh56TOY2FhDi93V2Rk5xrGElPSmdxI1EPkFZXi1OU4nIu5g8rqxjuyNUet0SIlMw8pmQ/mVe1trTBmUCRGDQqHnbVlR4ZLRF2sYaHXB50SdTodfvP3j/DvT39osN+Yof2x7Yt/wsmh45sL3Fe/q2NKepYh0fHIqYvNdnUsK6/EVxt24asNu+Dv44HVi2ZizeIZCAng5xsiIiIiIup4TG7s5X7x1r/x0dcbm1zv5eGK5x9dhqdXLez0jo0Pc3awxaMLJuKLLYcFk4q79kejpkaFBbOndpuKlcUlpXj/s2+Rm1cgGHd3dcLKBd0nTjE0Gg3OXLwqGAsJ9IO7m0unnE8mk2H+zCn4+Ov1gqRbtVqN42cu4HJMLCaOGY6B/SJZFcoI2NrawNXZSZD8m5iSxuRGIiOk0+lw9Mxl7I0+02i1wcaYmpjAydEBpqYmUCoVgB5QqdWoqa1FQWFxkxcPAXUXGsXcSkDMrQS4uzjhkflTERrIL8qJjI2lpTC5saqqBr//+3v46zsfNdh2+OD+2PH9J3B1ceqq8JpkZibs6piSloG9h47jx627cPr85Ub30ev1OHLiDI6cOIMXfvUmFs+ZjjXLF2Di2BG8II2IiIiIiIhIYjsOncLv3vkSsfEpzW4XGeKHT/7yKmw6OdFALpNh9Zyx+OtnPwkKvu7afwTmZmaYOLbzOke2l06nw/otO3H1xi3BuKuzI+ZOHStRVB3rcszNewVfHxjYt0+nnGvG5HFISk1HWXmFYezE2YuIvZOAKeNHITwkqEfMNfcWQf6+DZIbich46XQ63ExIw4nLtxCXmNHyDm1UUlaJ3ccuYu+JyxgYEYCxQyIR7OvJ130iI2RpUb9zo+rev7V49JU/Ycveow32WTF/Kr78129hXi8xsrP5+3gaujrW1qpw8sI17I0+3WJXx5T0bPzlv1/hL//9CqOG9MWaxbOwbM5kONjbNrkPERERERFRWzC5sZe7cj2u0fFRQ/rj50+txMKZE2FiYtLFUdUZEBGIVXPH4fudxwXjh46dQnFpKR5ZOAeWlhaSxHbf3aQUfLN+K4pKSgXjTg52eOmxJbCSOL6OEhN7WzCpBgDjRg3t1HPa29li6byZ2LRjL1QqtWBdRWUldh2IxvkrMZg2YQyCA/06NRYSLyjAt0Fy4/jRwyWMiIjaKregCOu27kdSWmaT2ygVCvj6eMLX2xO+3l7w9faEu6tzk4notbUqZGRlIy0jG2kZWUhNz0ROXn6j2+bkF+K9LzZg/IhBmD99HMzN2MGXyFjU79z4rw8/R2FRSYPtFs2Zhu8++pfkn/Gb4u/rjReeXIUXnlyFxORUrNu0A+s2bhN0G39YVVU11m3ajnWbtsPLww2rls7H2uULEBkW1MWRExEREREREdG+Y+ex6Lk/NFlsTSaTYe7kkXjp0YWYNGpglyUWODvYYNXsMfh6+zHB+OYde1GrUmH6pLHdJslBq9Xi2w0/4dLVG4JxM1NTPLF8LkwlmlPuSBqNBmcuXBGMhQYFwM3VuVPOZ25uhvkzJ2Pdph2C8cKiEmzcthd+Pl6YNnE0vDzcO+X81LGC/H1w/OxFw3JufiHKKyo7pfMnEXUevV6PK7eSsP3IeRSVVrS8AwAZADsbK5iaKO8Ve9VDrdWiplaN8srqZvfV6XS4HJuIy7GJ8HJzwtLpoxHi79kBt4SIukr9zo3VNbXILyzG/Cdfx7krsQ22f+Olx/Cn156WvKC/mZkppowdhiljhwm6Om7bdwxHTl1s8m+nM5du4MylG3j5zXcxb+pYrFk8EzMmjISJCS9FJiIikor83k9vx/uAyLjxL4pezqPeRMyAPmH47N+/x5D+nVN9sq1GDghHrUqDzftPC8YvXb2BhMQUrFwyD30jw7o8rtpaFXbsO4zjp883+EPewc4GLz22BPa21l0eV2fQ6XQ4dU44iefv6wWfLui6Fxzoh58/vRbHTl/A5ZibDe7rvPxCfL95B4ICfDFtwphOm1gk8QL9fHD2oe6fGVk50Gg0UCr5NkTU3d3v1rjz0CmoNZpGt3FydMDYkUMwatggWFu1fpLezMwUQQF+CAp4kKSekZWDk2cv4sLlGNSqVA32OX7uCm7eScSaxTPZxZHISNRPbmwssfG1nz2Jf775K8kn8VorKMAPb/3653jz9Zdw+vxlfLdxOzZt29OgIMh9mdm5ePv9z/D2+59hyIAorFm+AI8snA0XZ8cujpyIiIiIiIiodzp+PqbRi3Ntra3wxLIZeHHtAgT6SpNIMCQqCJU1tdi0/6xgfOe+wygtK8fCOdMkTxwsL6/Atxt+wq07dwXjSqUCz61ZBF+vnpF8F3PzNsorKgVj40Z2bsHXoAA/TBo7AtEnzzVYl5qeic+/24S+kWGYPG4k7O3YGac78/JwhZmpqWBuIyk1Hf37hEsYFRG1RXllNTbsPYVrt5Ob3c7NyQ4+Hi7w9XCGr4cLfNydmizMWlFVg/TsAqRl5yPt3r9NJU1m5hbive92YvywKMyfNBxmpsZfOICoN6if3KjVajFi3tNITs8SjCuVCnzy91/jieVzuzK8Vnu4q2NGVg5+2LYf323Zi7iElEa3V6nU2LInGlv2RMPFyQErFkzD2sUzMTDMt9sUaCEiIiIiIuPBrJJern9UGLbtizYs29vZdJvExvsmDIuCuZkJvt95XDDpWFpWjo+/+gHDBw/A0vkzu6zDy92kFKzbuB35hUUN1rk5O+Clx5bAwb7nTCzF3k5AcamwM+XYEUO67PzW1laYM30ihg3qh0PHTiMhKaXBNonJafgk5UcM7BuJiWOGw8amZySW9iT1L9zX6fTQaLRMbiTq5gqLS/H1pt1ISstqdH1URCjGjRqGyLDgDktI8vZ0x4rFc7Fg9lRcuByD46cvNOjmWFhcive+2IAJIwdh0cyJdRVQiajbMq83ofcwuVyOD/7xBzz/xMoujKjjyGQyjBkxBGNGDMF///Y77DoQje82/IQD0aeg1Wob3efStZu4dO0mXvv9PzBryjisWb4Ac6ZNhBk70hIRERERERF1Ggc7G8GytZUF/vH601i7aBqsrbpmjrE544dEoqZWjZ1HLwnGj58+j9sJiVi7fCEC/Hwkie3ytZvYuG03KiqrBOMKhRxPrZiPsCC/JvY0LjqdDqfOC+//AF8feHdBwddxo4bB39cbB4+eQkZmdoP1N27dQVx8IoYP7o+xI4Y0+30bSUehUCDA1wu37z5IikpOzWRyI5GRuBybiI37TqGyurbR9Rbmphg5IAxjB0XC1cmu1ce1tjRHRJA3IoK8DWOpWfk4efkWLt28C7Wm4VzC8Qs3EZuQhtXzJiDEj10cibq7+smNABokNtraWGHLJ3/DlLHDuiosUbw9XPHrF9bi9efX4MqNO/hu6178uP0gChopYgsA+YXFeP/LjXj/y43oE+qPNQunYdX8KfByd+nawImIiIiIyGgxq6SX6x8ZKliOuRUPvV7f7arnjOgfBgtzM3y3/ShqaoVdnM5fvobbCYmYPG4URg4bCCtLy06JITU9E8dOnceFK41Xdg3y88LTK+fBxqpzzi8FvV6Pk+cuC8Y83V0R6N/1k6euLk5YtXQeklLScCD6FHLzCwTr9Xo9rlyPxY24eEwcMxwjhw7sds/j3kylUjcY4wX8RN1bdl4B3v9qM0ob6ULm4uSINcsXIDjQv9POb2FujvGjh2PsyKE4dvo8duw9DLVa+Fpy7OwV5BeW4OmV82HKyqVE3VZTRUisrCyx8Yv3MGvqhK4NqJNYWJhj2YJZWLZgFnJy8/HjT7uxbuM2XLsR1+j2Go0GO/dHY+f+aDjY22HZgpl49JGFGD64Pz/HEhEREREREXWwAZFBgmW9Xo9nV86BQtF9CqdNH90fpiZKbDko7OCXm1eAf3/4BSaPH4W50yfBpIu6OJaXV2DDtj24ej22wTpTExM8s3ohIkMCuiSWrnAzLh7FJfUKvo7suoKvvt6eeHL1UsTGxePw8TMoKS0TrNdoNDh9/jKuXr+FCWOGYXD/qG71/KU67q7OguTG6prGk6SIqPuoqq7F+j0ncDWu8W6Nvh7OGDekDwb3CYKpScdcZufn6QI/z/FYOGU4zsfE4+TlOOQVCd+DCorL8N63OzFhWBQWTBkJExZ7Jeq2GktufJivlzt2f/NvRIUFNbtddySTyTC4XzgG9wvHv3/3c+w7egbrtu7DrsOnGr0WDABi41Pwm39+hv97+3NMGT0YaxZNw8JpY2DVRY0riIiIiIjIODG5sZcb0CdMsFxcUob0zBz4end+Bcq26h/mj989txTrd5/ArcR0wbrSsnL8tPsAdu0/gsED+t6rbukl+pwqtRqXr93EiTMXkJqe2eg2Jkol5k0dgwkjB3ZY16ru4s7dZOQVFArGxo0aIunF1oH+vnj2sUcQc/M2ok+eRXlFpWC9Wq3GwaOnYKJUYuigfhJFSfXVqoRJyaYmJrxon6gbS83IwYffbkFlVbVgXCaTYcKY4Zg/cwpMTbsmQVkul2PS2JGICg/Fuk3bkJicJlgfG5+ED7/ZjOfXLm5x0oCIpGFpYd5gzNPdFbt//AwD+kZKEFHnc3dzwavPP45Xn38c12NvY92m7fhh807k5OY3un1xSSk+/WYDPv1mA0IC/bFm+XysXjoP/r7ejW5PRERERERERG3TPyJYsFxZVYPE1CyEBkrTDbEpE4f1gaW5KdbvOQWNVmcY1+v1OHzsNG7cuoO50yehf1REpyW2VdfU4MLlGOw5eLRBt0YAsLa0wLNrFiHIr+d8b6HX63Hy7EXBmLene5d3y5TJZIiKCEV4SCAuXLmOE2cvoqZeclxVdTX2HjqO85djMGfaRMk6elLj1BqNYNnMjIUZibqz0vIqfPjDHmTlFzdYZ2VhhuUzx2BQZGCnXdtgZWGOSSP6YcKwKBy7cBM7j15s0Mnx2IWbyC0owdPLpsOMxV6JuqXG5kLvG9w3HDu/+hc83Jy7MKLOYWKixLxp4zBv2jgUFZdi0+4jWLd1H85evtHo9nq9HodOXcKhU5dgbWWBJTPGY82iaRg/vH+Pu8aSiIiIiIjE60bJjR35RZCRJsw00g2wjQdo8x6+Xm6wt7NBSWm5Yexa7C34erm07UAaERUHFa2P28HaFC8sn4yzMQnYevgCamqFFYDUGg3OXbqKc5euwtfbExGhQfD19oSvtyccHexb/MJRrdEgMysXaRlZSMvIxLUbcaiqrm5y+0BfT6xeNB1uzg73RtrwGEiW2NW68z7o2vhge1cXJ4QFB7X6GA1P3TG3Wa5QYGD/PugTEYKzF6/i9PkrUNXr5hV96hyiIsNgYfgCyUhfF0TrHre7tl7H1bqujd0jtg4l6jku9j1ASt379aw7EjcBJmLfVpw3MycfH3yzGVXVNYLxrujW2BxXFye8+vwTjXZxvJuSgY+/24oXH1vWszo4iv5sSF1J7MR2Y53B23B2UecWpRWnHjFkoGC5X59w7P7xM3h7unf+ybuBfn0i8K8/RuDvv/8Vjpw4g3Ubt2PbnoOorvc6e19CUgr+8Pf/4g9//y/Gjx6GNcsXYsncGbC1tYH4zwti9jfW1yTjeJ5QHTGvhSyeIgXj/PuHzxUiIiKi3sndxRFuzg7ILXiQvBBzO6nbJTcCwPB+IfDxcMa6nSeQll0gWJebV4Av1m2Cna0NRg8fjNHDB8PB3q5DzpuVnYvjZy7gwpWYBvM69w3oE4pH5k2DrY1Vh5yzu7gdn4j8wiLB2NgRQyX7+0GpVGLUsEEYEBWB42cu4OLVG9DpdIJtCotK8P3mnXh85eIO+J6NOkqDOdEuKhRJRG1XWFKO97/fg4LisgbrBoQH4JFZY2Bj1TVdxuRyOSaN6IeoED+s23UMSem5gvVxSRn48PvdeH7lLFiy2CtRt9NUEeZ5U8fghw/+1CM7Fjo62OG5NYvw3JpFSEhOw7qt+7Fu616kZuQ0un1FZTW+2bof32zdDx8PV6xeOBVrF01DWKBvF0dORERERETdVTdKbiQpyGQy9I8MxfGzlw1jMbEJmDdtvIRRNU8mk2HUgFBEBHph/YELuBWf0uh2dQmKWYZlK0tL+Hp7wNnJESYmSpiYmECn1UGt0aCmpgaZ2bnIysmDVqtt9HgPMzFRYt6U0T2yW+N9yanpyMwWfmE6ZvjgbnURoKmpKcaPHo5B/frg6KnzuHI91rCuuqYWx89cwIzJ4ySMkO6r37mRE3lE3VNeYTE++HpTg8TGAD8f/OzJ1bCU+Ev3+10cA3y98eEX6wSJQXdTMvD5+u14dvUiKJWdU7GciNpn3szJ+PMbr2LnviMYNXwQ/vjrn9cl6vWyBF6lUonpk8Zh+qRxKCsrx5Zd+7Fu4zYcP32hyX2On76A46cv4MXX38LC2dOwZvkCTBk/Ckol/5QnIiIiIiIyTnp0eKEHMX9fS7UvAOhbno/raAMignDg5CXD8rXYBCydMbZNx9CLiFvWhsfe08Uev3x8Dg6fvYG9J64KujgCQGlZOfYeOob9R06gX59w9AkPgZ+PFzzcXFrd0VGlUiEjKwepGVm4GhOLu8mpTW5rZWmB5fOmYHDf8HtzhT3ne526gq+XBGNuLs4IDfZHu2+nqN+PB/taWppj5pRxGDaoLw4fP4u4+ETBllqtFvsOH8dTa5Y+mMPtoHO3b3eJzi1BEemmzl1bWysYMzM1AfS6+ntJrxvdZ112XrG64+PYFWQirocRc5+JOW8rlJRV4r/rdqOwpFww3hXdGpvj6mSHV9fObbSLY1JGLj5avxcvrpkL804p9irVtUBi33uk+t2UMG6diH3F3l9SvhaKeV2Qd+7zW64A+kWE4HpcgmHs508+gnfefAUKUXWvpSxw2/prLkKCAvCn15/HW798FqcuXMO3m3Zjy55olFdUNrp9enYe/v7RD/j7Rz9g2IA+WLNkFh6ZPxVODvZ1p9a0/7HWqxo/Z+t2lvBzRie/7zV/7u5zLWjbiHktFHmbZVKdW+xjJeazmchzS/a5sOd8d9Emkn1X2Mjj3Fv/jpKADEb8kt6BeBcQGTdeEUno36decuOteAmjaT0HWyu8sGYhElOzcPLCNVyNTYBW2/QHocqqqnsTPolNbtPiOe1sMWb4AIwe0h82lj2oM1QjTpwVTuI52NshKiJUomiaZ2NjjXkzJ6NWpULs7QdfFF28eh1DB/aFk6NDM3tTV2i8cyMRdSdqtQaffPcTysqFX/aGhQTi2cdWwNys+1QBDfDzwS9eeBIffPYtysorDOOx8UnYtv8ols6ZImF0RFSfXC7Hb197Ab997QWpQ+k2bG1t8MSqpXhi1VKkpGXg+007sG7TNiQkpjS6fU1NLX7cugs/bt0FDzdXrFw6F2uXL0TfyLCuDZyIiIiIiIjIiPWrl9wYE9f+OcOuoJDLMX10f/QN9cW63WeQlpnbYBudTodrN27h2o1bAOqKK3l7uMHH2xNuLk4wNTWFiYkSep0eKrUGtapaZOfkIS0jC9m5+dC34qK1um6NU3tct8b7klLSkJVTr+DriCHdquCrk6MDli+chdT0TBw8elpQoDYzOxc34+L5PVE30bDga8++poDIGFVU1eCDH/Y2SGx0cbTFS6tmw8neRqLI6tzv4hjk647/rd+Hyupaw7rkjFx8vnE/nlsxCyYs9krUrXz+79/i2df/hvLKKvzmxcfw5Ir5dSt0XV/URSpyuRzjRgzCuOED8cFffoUdB45j3Za9OHjifIMu5PdduBaLC9di8Ys//gezJ43G2qWzMWvcIJjyMxQRERERUa/D5EZC/0hhwtq1WONIbgTqujgG+3sh2N8LiysqcebSTZy6eB3FpeUt79wGESEBGDdiIKLCgqFQ1FUC0WtVLexlvNIzs5GSliEYGzN8cLfvUjll/CjcTkg0JLlqtTocPn4GyxfOljgyajiR132SpIiozq7Dp5CTXygYCwrwxfOPr4RpN+y26uXhhhefXov/fPyVoIPjsbOXMbBPGIIDfCSMjoio9fx9vfG7X/4Mv33tBZy/dA3fbdyGjdv2oLiktNHts3Pz8M6HX+KdD7/EgL4RWLN8IVYungs3V+cujpyIiIiIiIjIuAyICBIsx8QlSRRJ23i6OOBXz67ClZt3cOL8NSSmZja5rUajQUp6JlLSm96mNWQyGfpFhGD8qCEIC/DqVol+He1kvYKvjvb26BMeIlE0zfPz8cITqxbjo6/Wo7CoxDB++PhZhIcEwsSEF4FLrbZWLVg264bzK0S9mU6vx5dbDyOnoEQw7unqiBdXzYKdtaU0gTXCz9MVr6ydiw9/2IvSiirD+O2kDGzedwor546XMDoiqm/ogD64cvAHqcPoNiwtzLFiwXSsWDAd2bkFWL/9AL7bvAc3bt9tdHu1WoPtB45j+4HjcLS3xSPzJmPtoukY2j+8R/8tQkRERERED3TvTCXqEgP6CKs4JqVmCrogGQtbayvMmDAcf/zFk3h+zQJMGzcU4UG+sLRoexKVq7MjhvSPxOJZk/DWa8/gpSeWo39kqCGxsaerP4lnY22F/lHhEkXTeg72dhgxeIBgLC4+sUGiJnU9dm4k6t6S07Jw5NRFwZiXhxteeGJ1t0xsvM/b0x0vPrUGJsoH9Tr0emDd1r0NkqqJiLo7mUyGEUMH4qN//wlZt85g89cfYt6MyVAqm65JdO1GHF773d/gHTUGcx55Chu37REkfBMRERERERHRA/3rJTdm5hYgv7BEmmDaSKGQY2j/CLz2zAq88eJajBnav1M6wtlaW2HmpNH4869fxLNrlyI8OKBHX0yclpGFlPR6BV9HdO+CrwqFAlMnjBKMlZaV49ylGIkiooc1KPjKOVGibuXEpVuIT8kWjLk62uHnq2d3q8TG+zxdHfHS6tmwqnfd0+krtxCXmC5RVEREbePh5ozXnl2Fa4d+wJUD3+OVp1fA1dmxye2LSsrw0XfbMGLBc4icvBZ/+9+6RrvYExERERFRz8LOjYTI0EAolQpoNFrD2PW4uxgzbIB0QYmgUMgRFRaIqLBAAIBer0dhcRnSsnKRkZ2H6ppaqNQaqNUaKBRKmJgoYaJUwtHBFr5e7vDxdIOFubnEt0I6Obn5iE9MFoyNGjao2Yuqu5OxI4fi6o1bqHroou4D0SfxzKMrevTka3fXsHMjJ/KIuguVWo3vtuyFXq83jCkVCjyxaiksLLr/+2GAnw/mzZyMrbsOGMbyi0qw8+AJLJ0zRcLIiIjaz8zMDIvnzcDieTOQX1CIDT/twbqN23Dp2o1Gt9dqtdh76Dj2HjoOO1sbLF0wC7977QX4ent2ceRERERERERE3VdogDfMzUxR81BBxpjbSZgyepCEUbWdt4crVi6YioUzxuHCtVv4f/buO6yp8+0D+DcJCXsP2aKggLhQUWSIe2tdrbbaYVu716972r3bt8tO22prte69xQUKCm7FLSp7751x3j+s6CEgI0AIfD/XxaW5z7oTsjjPuZ/74tVkJKVmIje/sNH7kkgkcHZygKebMwJ8fdA3wBdGRrIWyLptio4VT/pnZWmBPj399ZRNw/n6dIWXpxuuJd3q0Bl96AgCe/nDwrztFed0JBwTJWq7cvKLsGF3nChmY2mOZ+dMgKW5qZ6yqp+Loy2evm88vv17E6qUqur40o378NaT98DUpPETvhMR6YNEIkHfgO7oG9AdX7z1LHbuP4wla7Zi/Y79WpPm33QhMQlvf/k73vnqDwwbHIj7p43BtLFDYNkGC9KJiIiIiEg3hlGtRC3K2FgB/25dcPrc5erYiYSLBlvcWJNEIoGDnTUc7KzRr2d38TIZBxNup9FosDsqVhQzMzVF/z4Besqo8UxMjDEsLBhbdu2rjqVnZuNUwnmDGIxsr9i5kajt2hx5AJk5eaLYhDHD4OLspKeMGm9Y+GAcP30WidduzVC6L/YoAgN84dPFQ4+ZERHpztHBHs8+9gCefewBJJy/iCUr1mPpqg1ITa99htLComL8/vcKrNm4HQe2Loe/r08rZ0xERERERETUNhkZydDLtwviT12ojp08d8XgihtvMjUxRkRwICKCAwEAJWXlSE7LRFJqJpLTs1BSWgalSg2lUgWJVAqFkRGM5EawsbKEp5sLPN2c4e7aqcMWX6VnZOFS4jVRLCSoH2Sytl/cKZFIMGZYGH77e2X1xIVVVUrsPXAYk8YM03N2HZvWmGgLdFglosbTCAL+2RQlKg4EgAfuGgo7aws9ZdVwnV0dMW1UMJZvPVAdyy8qwbpdsbhv0lD9JUZE1ERGRkYYPyIU40eEoqCwGKu37Mbfq7fgQFzt3cgFQcCemGPYE3MMT7/zDaaNDcdzc2dgQG+/Vs6ciIiobZL+99PR8TEgMmwsbiQAQN8e3UXFjSfPXtRjNqQParUaazbt0BrECx7QFwoDG9Ts37cn4o6dQnburWKd3VEx6OHrA7mcA0j6cHsnTYCzlBK1FVk5edh94Igo1tnDDSMjQvWUUdNIpVLcP3MqPvn6JyhVNwYlBQFYvmkX3np2Ljv3ElG7EeDXDZ+9+wo+fvtF7I0+hCUr1mPN5h0oKyvXWje/oBCTZz+OQztXw97OVg/ZEhEREREREbU9vf26ahU3thcWZqbw9/GCv4+X9kIZuzrdThAE7D1wSBQzNzND/7499ZRR47k4O6FPTz+cOH2uOnbs1FkM6tcbTo72esysY6usUopuc8JXorYh6shZXLqeLoqF9+8B3y5uesqo8cL6+ePY2URcvJZWHTt47BwCe3jD35uTvRKR4bKxtsSj903Bo/dNwZVrKfhn9SYsWbsDiUlpta5fVl6Bf9btwr8b92Dx129g9pRRrZwxERERERG1BBYoEwCgd49uotsnE1jc2JGoVCqsWLcVZy9cFsVNTU0wsF9vPWXVdFKpFKOHhYliRcUliIk7pqeMKDcvX3TbxtpST5kQ0e2iDh+vntUZAGRSKebcfZdBzExdUydHB0weN0IUS8vIxqWryXVsQURkuGQyGUYODcVfP3+JjHOxWPzjFxgxJESrmPvK1STcPfdZKJXKOvZERERERERE1LH09fcW3T55PlFPmZC+aDQarN+yCxevXBXFgwf0NbhJUoeHB0MuvzWftSAI2LnvoB4z6tiUSiXUarUoxglfifSvpKwCG3bHiWJ21haYMmKgnjJqGolEgjmTIrQ6wi7fEgWNRqOnrIiImpe3lzvefeEhXNq/DNGrF2DevZNgbVl7h121Wo1HXv0csUfPtHKWRERERETUEljcSACAvgHdRbdPn78C1X+dj6h9q6qqwrI1m7QG8IyMjDBj0liYmBjmTK4+XTvD28tTFDtw6CiKi0v0lFHHpVarkZtfIIo5OtjpJxkiQmlZOc5fvoYtuw9iX+xx0TK1RoOP/+8nPPva+3jxrY/x/hffY9HS1di9/yAuXbmGiopKPWXdMMPCB2vNSB11+HgdaxMRtQ8WFuZ4YNZU7Fr3F66d3I8BfXuJlu87cBjPvPa+qJidiIiIiIiIqKPq20Nc3HjuShIqKqv0lA21NrVajdUbt+FkwjlR3MTYGEGBhjfhq5WlBUIH9hPFLl+9jsuJ1/WUUceWm1+oFbO0MNdDJkR0u0MnL6JKKb7+afbEITAxwM6q9jaWmDpykCiWk1+EhEtJesqIiKhlSCQShA7ohV8/fRnp8WuxfMG7mDB8sNZE3VVVSkx9/G0kpWbqKVMiIiIiImouRvWvQh1BnxrFjZWVVbiYmIQe3bvqKSNqDZWVVVi6eiOSUtJEcYVCjnunTUSXzh56ykx3EokEo4eF4ZfF/1ZfyK1UKbEnOhZ3jR+l5+w6lrz8Aq2ZAh3t7etYm4haQlJqBqLjTuL8leu1Dq7XpFaroVarUZFVicysHMQfPwXgxnurk4M9Avy6IXxwEDo5ObR06o0ilUoxJGQgVm/YVh07kXARhUUlsLaqfTY/IqL2xMPNBeuW/ISBI6cjPTOrOr7wrxUI8O2G5x57QI/ZEREREREREelfb1/x2KdKpcbZS9fRr2c3PWVErUWlUmHl+q1aE77KZDJMnTDaYCd8DRkYiKMnE1BcUlod27H3ALp6eUAq5VzXrSk7N09029bGCgqFYXUDJWovqpQqpGTk4np6NrZGHdVavmDpVhgZySA3MoKluQncnR3g6eIATxdHeDg7wNSk7RY+hvXzx8Fj55GckVMdizpyBr18vfSXFBFRCzIxMcY9E4fjnonDkZmdhze/XIhFK7dWL8/Kycddj76J6NU/wJxfvYiIiIiIDBaLGwkA4GBnAzdnJ6Rm3LoA9kTCRRY3tmPl5RX4Z9UGpKaLZy4yNlZgzt13wcPNRU+ZNZ9OTg4I7NUDx04lVMdOnDmHQf37wrmTox4z61iyc8QDeZYW5gY7QExkSKqUShw7fQFRh0/gWkp6s+xTEARkZucgMzsHe6Jj4dutK4aEDETvHr5aM+TpS/CAvtiwNRJKpRIAoNFocPDISYwfHqrnzIiIWoebqzPW//MzIibdJ+q4++Lbn8DXpwvGDA/XY3ZERERERERE+mVpYQZvT1dcSbo18efJ81dY3NjOVVVVYfnaLUi8Lu5sZSQzwsypE9DN20s/iTUDhUKBEUMGY/3WyOpYdm4ejp1KwIC+vfSYWceTnZsvuu1ob6unTIg6puy8Ihw8dg4Jl5ORnlNQPQl2bQQASpUaSpUaZRWVyMwtxNGEK9XLHe2s4NvFDWH9/OHh3LYme5VIJIgICsA/m/ZXx85eTkZ2XiEc7az1mBkRUcvr5GiHXz95Canp2dgZHV8dP3nuMh7438dY9f3rnOCDiIiIiMhAsbixNhKJng6s6x9WdZ+Yq39TNfoGdNcqbrxv6lgdc2oAVXmTN5VIdO3+psvvWsfniUSH3/cdTsLWf1wJSkvLsGTlemRk5Yie76Ymxrj/nilwdenU9P3f+eAttN+6DQ8fjDPnLqLqvyIXQRCwY080Hpg1FRK9vdY7lpqzlLJrY134fDQkbfn9o6S0DDuj4xB75DRKyyta9FgXLiXiwqVEWFtZInxwEIYPGQwTY/0WL5uZmmJgv944ePjWLKwH4k5g7NDB+jmJrctzRZfPe2oiXV7b/H01mi7fh9G234vv5E4XVNSvYfc5qF8fLP7xC8x65PnqmEajwaxHX0DsjlXw6+7d+EPr9BTn66P1tfzzrK3R7bWlG93fj/gaISIiIiJqTX39vUXFjSfOXbnD2mToKisrsWz1JlxPSRXFFXI57p0+CV06e+gps+bTp6cfDh89ifTM7OrY3gNx6OnXnROOtqLsnJrFjXZ6yoSo49BoNEi4nIyoI2dx7kpKs51ly84rQnZeEQ4cPYeu7p0QPqAHAv27Qm7UNiZ77R/gjbW7DqHstgkOo48kYNroED1mRUTUOoyMjLB8wbsYPPUpXEi8NXnJ+p0H8M43f+Pjlx7SX3JERER6IpHosfylDeFjQGTYWNxI1Xr36IYtuw9U3z519pIes6GWUlxcgr+Wr0NOnnhwxcLcDPffMwWdnNrWrHO6srAwR1jwAOyJjq2OXU1KxsXLV+HbjZ1JW4NWcaMDB/KIWsrxMxfw78ZIlJSW3XE9IyMjuLt0goe7KzzdXdHJyQHGCgWMjIwAQYBSpUJlZRXSM7OQlJKGpJQ0pGVkQaPR1Lq/wqJibN6xB7Hxx3H/PVPQ3adLS9y9BgsfHCQqbiwoKkFKehY83Zz1mBURUeu6Z8oEnL1wGR988UN1rLCoGJNnP45DO1fDztZGf8kRERERERHRbXSdTFOH7fU5MZdEh+IAHfPu7d8Va3ZEV4dOnUts+v4aQ6Nu+ra6PF4dloDy8gr8s2oDUtMzRUuMFQrMvnsyPN1dUeeEMzo9x3WZlLjx20oAjB4air9WrKuOlZaV4cDhIxgZ0YhCF50nDmrd+90sx9X52Lc0unOjXic41OdjpsvzpPZxqtahr+e3vukp93p+11VKFfbHn0XU0XPIKyxp0VQSUzKRmJKJNTtjEdLXDyNDesPc1KRFj1kfhdwIwX27Y8+h09WxQyfOY+LwQVDIm3gpoE5XAXfAK4h1fT/S2+eeHj/rdaWvK9V1/rtHl8leDXRiXp0/r+t/zGysrbHxj88QPOVx5BcWV8c/+3Ulevh2xZwpoxp/WI2q8ds0Gx2eJ1IdLwE31E6Xurw2DfZ7uA7nFAAAOpxX0Pm9sAN+VyAiIqJGY3EjVesb0F10+/iZCxAEwWA7opC2gsIi/L1iPfIKCkVxK0sLPDBzChza6eyRg4MCcfTkGRQW3TqZsWvfAfh07QyZjIPBLS07p2bnxvb5PCPSp5LSMqzYtBtHT5+vcx1bG2uEBfdHrx5+cOnk2KD3v27eXtX/VyqVSE3PwonTCTh4+BhKy7QLKHPz8vHtL4sQEToId40fqbcujp7urnB0sBO9/ySlZrC4kYg6nPmvPItzFy5j1YZt1bHLiddx90PPYvvqPyGXy/WYHREREREREZF+9PX3Ft0+ce4Kx0TbodLSMixZuR4ZWTmiuKmJMe6/ZwpcXTrpKbOW0aWzO/y6dcX5S7eKdQ8dOYkBfXvCxtpKj5l1DCqVSmsMvt7iRiJqksTkTCzZFI2svMI7rieRSODsYANPFwd4uDjCxdEWJgr5f90XJVCpVahSqpCZW4iktGwkpecgLSsP6jomey0pq8DOmBOIPXkB904IRx9fr+a/c40Q3q+HqLixtLwSiUnp8PM2/I7EREQN0a2LB1b9/CHGPvASVKpbRV/zXv8aPp3dEBzYQ4/ZERERERFRY7G4karVLG7Mzs1HRlYuXDq1r05+HVVefgH+Wr4OhcXiWetsrC3x4KxpsLWx1lNmLU8ul2PEkBCs3byjOpaTl4+jJ85gYP8+esys/dNoNFpdQtm5kah51det0b+7N4aEDERP/+46FXTL5XJ4ebrBy9MNE0YPw7FTCYiKicPV6yla6+4/eBhnzl3UaxdHT3dXreJGIqKORiqVYtGCL5B4LRlHT56pju89cAjPvv4Bfv7qA164SURERERERB1OzeLGwuJSXE/NhJc7J0drL4qLS/D3inXIzhVPwGluZooHZk5FJ6f2Of49KiIEF69chUZzo6uGSq1G5P5YzJg8Rs+ZtX95+YXQ1CiIcrBjcSNRc6pSqrBp31HsPXymzp5HpiYKDO7ji77+XeDeyR7Givon+OvW2RXo5w8AUKrUSM/Ow+mL13Hw+HkUFmuPvxaXluO3lTsxoKcP7h4TAgsz/XRxdLK3hqeLI5LSs6tjSenZLG4kog5leEh/fP/eC3jq7a+rY5VVSkx9fD4Or/sRnm7ta0ITIiIiIqL2zEB7ilNL8PZyh7mZqSh28uxFPWVDzSk7Jw+Llq0RdS4EAHtbGzx834x2Xdh4U68evnB1Fp+w2HfwMMrLK/SUUceQX1AItVotirFzI1HzEAQB63fsx8J/N9Za2Ng7wA/vvvYcnn3sQfTp6d+snWrlcjkG9e+LV559DK+/8AS8vTy11snNy8d3vy7GvgOHm+24jeHp5iq6nZSWqZc8iIj0zczMFOv/+QUunZxE8d8WL8eChUv0lBURERERERGR/ri7OMLW2lIUO3kusY61ydAUFBZh0bI1WoWNVpYWmHvf9HZb2AgA9na2COrbSxQ7c/4SklPT9ZRRx5GdK57s1drKEsbGCj1lQ9T+JCZn4tOF67GnjsJGTxcHzJ40BJ+8MAfTRw+Gt4dzgwoba5IbyeDp4ogJEQPw4bP34dEZI9Hdy7XWdY+cuYyPflmFkxeuNfo4zcXTVfyZlpSWXceaRETt1xNzpuDpB6eJYpk5+bjrsXdQUlqup6yIiIiIiKixWNxI1dIysrW6dly6mqynbKi5ZGRmY9GyNSguKRXFnRzsMfe+6bCysqxjy/ZFIpFgzPBwUaysvBzRh+L1lFHHcHvXNAAwNzODWY0iaiJqnCqlEleupeDr35ZhZ1Sc1nIzU1M8dN90PP7Qvejk2PIXaXi6u+J/Tz2MGZPHQi4XDxIKgoCV67dgW+Q+CEJdc6i2UF4e4oHGtIxsqFTqOtYmImrf3Fydsf6fX2BiYiyK/++tj7Fzb7SesiIiIiIiIiLSD4lEotW98cS5K3rKhppTXn4BFi1bjbyCAlHcxtoSc++bDocOMAFnREgQTIzF54B27DnQ6ufoO5qaxY2O9uzaSNRcDhw7j//7ewuy8gq1lnm5OeHlh6fgtUenIaSvHxRyo2Y7rkwmRaB/Vzx//0S888Td6NlNe7LXm10cN+yO08v7rKeLo+h2cjqLG4moY/rmnWcxKjxIFDt57goeeOlTre7aRERERETUNrG4kQAAarUaDz7/nlbnJ38fL/0kRM0iJS0Di5evRVm5eBYil06OeOjeabCwMNdTZvrR2cMNPXx9RLHDR08iL79APwl1ADVnxWXXRqKmKSkrx67oeHy64G/87/1v8fXCf5GYlKa1Xu8AP7zzyjMY2K+P1oQFLUkqlWL4kBC89eJTtXZx3LR9Dzbv2NNq+QDanRtVajXSMjmgR0QdV1C/3li04HNRTKPRYObDz+P8RV7ASURERERERB2HWq1GWXmFKHaRE74avOycXCxathqFRcWiuL2tDebeNwO2NtZ6yqx1mZmZIiJkgCiWkp6JhPOX9JRRx8DiRqLmpVZrkJKZi9/X7MG/Ww9qFQ4ayWSYMmIQXnpoMrq4ObV4Ps6Otnhi5hg8MHkoTE20u7LujDmB5dsOQNPak726iCe6zckvQmlZRR1rExG1X0ZGRli+4H107+Iuiq/feRDv/N8iPWVFRETUeqQSgT///RCR4Wq+KavIoH3x09/YG3NEFJsxYQRGhA/UU0akq+vJqVi6eiOqqpSiuIebC2bPmKzVtaWjGBkRiguXr0KtvtG9S61WI3L/QdwzZYKeM2ufanZudHRgcSNRQwmCgOspGYg6fAJHTp+/Y9dBiUSCe6dPQuig/q1a1FiTk6M9/vfUw9gWuR9bdu4VLdsWuR8mJsYYNTSsVXIxNTWBrY018gtuzeJaUFQMTzfnVjk+EVFbNHPqBJy9cBkffrmgOlZYVIzJsx/HoZ2rYWdro7/kiIiIiIiIiFrJF7+txOGT50UxL3eeNzRkGZnZ+HvFOq0JX50c7PHAzCkdbsLXgf16I/74GeTddn48cn8s/Lp1hZERLxFpCTUnfHWwY3EjUWNVKVU4ciYRh05dQlJ6DpR1jI16uTnh/kkRcHZs3deZRCLBoD7d4dvVDf9uicaZS0mi5QeOnoNKpcbsSRGQttJ4raujHYxkMqjUtx6r5PRs+Hl7tMrxiYjaEltrS2xc+BGCpz2DgqKS6vinPy2Dv48n5kwZpcfsiIiIiIioPuzcSDh87DTmf/mrKObh2gm/fvGmXgskqOmuXE3CP6u0Cxu9PN1x/z13ddjCRgCws7XBoP59RLGzFy4jKUW7A1pr0Wg0SElNR2pahtaMg4aOnRuJGk8QBBw9fR6f//QPvvhlKQ4dT7hjYaORTIZHH5iJsOABbeJzWyqVYsLoYZg5VbtofN3mnTh89GSr5WJiLJ45tUqparVjExG1Ve+++ixmTB4ril1OvI6ZDz/f7r6LEhEREREREdV0+MQ5zP92sSjm6eqElx+9Wz8Jkc5S0zKw+N81WoWNLp0c8dC90zpcYSMAyGQyjBoaIooVFBXj8NFTesqofVOr1cjNLxTF2LmRqOEycwuxeudhvPndcizdcgBXkjPrLGwcE9oXLz00udULG29nY2mOJ2aOwazxYVpjs4dOXsTanbGtdq5dJpPC2cFGFMsrLKl9ZSKiDqB7Vw+s+vFdyGTiy6Lnvf41Dh0/q6esiIiIiIioIVjc2MEVl5Ri9lNvVHexA24UJfyz4EPY2lg1yzF4gWzrunApEcvWbIJSKS5s9OnSGbNnTIJCoahjy45jyOCBMDM1FcV27Ilq9eeqWq3GidNn8dMf/+D3f1Zi4ZIV+GXRMpw5dxEajaZVc2kJGo2GnRuJGqmgqAQ/L1mHP5ZvRlJaZr3rS6VSzHtwFgJ79WiF7BonInQQZt99l1Z8xdrNyMsvaJUc5HK56LZKxeJGIiKpVIrFP36B/n16iuK7o2Jw8PBRPWVFRERERERE1PKKiksx+3+fQq2+NQYjlUqx5OvXYWttqcfMqKmuJ6fi7xXrUFFZKYq7uzrjwVnTYGZmWseW7Z9ft67o7O4qikXFHkFpaZmeMroxNpqanomSklK95dAS8guKRNdbACxuJGqIS9fT8cPS7fjg5zXYG5eA8oqqO64/fVQwJg8fCKlU/5e6SSQShPfvgUemj4CsRj57485gV4z+JntVcjyUiDq4EaH98P27z4pilVVKTH18PtKzcvWUFRERERER1Uf/Z3xIr55981MkXk8Rxd58bi7CBwXqvO+i4lLMee59mPmOwMh7n0dhEWcHa2kJ5y9hxfqtWoMnft26Yta0CVpFHh2ViYkxIkIGimKp6Zk4ffZCqxxfpVIh/tgp/LDwb6zfugs5efnVyzKzc7B64zYsWLgEx06e0fpdNkVVVRVy8/K1Cl5bWmFRMVRq8Ylzdm4kqp0gCDh8PAEffrcIZy4kNmgbiUSCh+6bjl49fFs4u6YLHdQfUyeOFsUqKivxz6oNrVJQXnO2VI2GEy4QEQGAmZkp/vjhU614c3z3JCIiIiIiImqrnn3vByQmp4tibz11H8KDeukpI9JF4rUk/LNyAyqrxMUwXh7uuP+eKTAxMdZTZm2DRCLBmOFholhlVRX2HYxr9VyUSiUOHTmJ735bgoVLVuPrn//Cmk07kZmV0+q5tITs3HzRbQtzM5iamugpG6K2r6JSiX+3xuDbJdtw/mpag7a5Z2wohgf3buHMGi/Qvyvm3T0KUql4THLT3ngkpbfOe5zcSCa6XVfXSyKijuTJOZPx1P3iybgzc/Lx/eJ1esqIiIiIiIjqY6TvBEh/Nu+Kwt+rNotig/v3wvz/ParzvtVqNWY/9z627IkBAOyJOYpn3vk/LPluvs77ptqdPHMO67dGahWL9PTvjqkTRkEmk9WxZcc0ILAX4o6dQm7+rcGmyP0H4d/du8WKQKuqqnDkxBnExB1DSemdZyTNKyjAxu27sT8mDiFB/dCvT8Ad81IqlcjLL0RefgFy8wuQl1+AvPxC5Obno/i/2U9NjI0xPHwwBgT2apXZDGt2bTQ1MYG5uVmLH5fI0BQWlWDp+p11FjXa2drAwc4GF69cE8XHjYzAgL5t/6KbUUPDkJGZjdj449Wx8xevICbuGEIH9W/RY9cs6pbL+dWXiOimk2fOi26bmZliUP+++kmGiIiIiIiIqIUtXR+JJet2iWIh/XrgnWfm6LxvQRDwxW8r8e2ideji7owVP7wFDxdHnfdLdbt4+SpWrt+qNcmmT5fOmDn15oSvnOzO1dkJfQJ8cTLh1gSvR04mICiwF5wc7Vv8+BUVlYg/cQax8SdQVl5RHRcEAafPXcLpc5fg6+OFIYMHwM3ZSefjKZVKVFUpYWZmqjX5YUvKzhWPibJrI1HdLlxNwz+bDyCvsOGTo08eFoSIoIAWzEo3vbp3xoN3DcPidXuqP3k0goAlG/fhtUenwqiFr9WR1CisFDjZKxERAODbd57Gtn1xuHrbBDflFZV32IKIiMhwScCOZ8CNx4GIDBev8O7AVm/aqRVzcrBDWXkFrCwtdNr3W1/+Vl3YeNO/GyPxzvMPoXtXT532TdqOHD+NzTv3asUDe/XApLHDW6WQzdDIZDKMHhaGf9duqo4VFZfgn5Ub0NnDDY4OdnBysIe9nQ2MjHR7qywvr0DcsZM4dOQEyisq6t/gNoVFxdi2ez+iYuMxOCgQPl06I7+gEHkFhcjNy79R0FhQgKLi+k/+V1RWYmvkPpw4cw4TRg2Fm6tzU+9Sg2gN5DnYtepAIpEhSMnIxoJFq1FUol3wbGVpgRl3jYO9rQ2+/vEP0TJ3V2eMGxnRWmnqbPrksTh38QoKCouqY2s2bod/d2/Y2dq02HGVSvGFLSxuJCK6ZeuufaLboyJCO3xHAyIiIiIiImqfMrPz8NT870QxKwsz/PN/b8DISPeCg89+WYG3/28xACArtwDTn/oAh9d+zzGRFpJw/iLWbNoBjUYjivt188aMyWN1Htdrb4aHB+PshStQqm6cLxcEAbv2x2D2jEktdsyysnIcOnoSccdPo6Ki6o7rXrh8DRcuX0PXzm4IH9QfXp5ud3ztqFSq/8ZHb46X3hgrzcsvRFFJKQRBgIerM8aPDIdLp9YpMq7ZudHR3q5VjktkSCoqlVi3Ox4Hjp2vdbmZHPCyBs7WaHYY1NMHo0P7tnyCOhrQ0weZuQXYGnWsOpaWlYdtUccwaVhQix5bVaNTY3N8tyEiag9Ky8qRnJ4lig0N7qOnbIiIiIiIqD48s18rHQaa9DlGJTRu9q0+Ab5Ajc6NG3bsR/+x92PZT58iqG8jZj6T3CqeW7puB774eanWKhqNBp8sWILF37wjXiBTNCpv8XF1PSmnzxnLmud5dij+OLbviQZqDPIEBfbC+FFDaxn84UDqTd19uqCLpweuJiVXx66npOJ6Smr1balUCntbGzja28HJ0b76X3s723q7YZaUlCI2/jjij59CVdWN7mGSWh5/DzcXhA8OglqtRlRsHNIzsrXWKSsrx+79Mdi9P0ZrWV37rUt6Rhb++GcVBgT2wvDwwTA1NWnwto2Rk5snysvJwZ4D+dSO6P5cTkxKxY9/ral1ZrigwN64e8p4mJma4JNvfhZdpCGVSnH/zKkG1ZHXzNQUs2dMxo9//FMdq6isxL9rNuHpR+9vkWMqlUrkFRSKYhZmZlqfl22aPnNt5PfKNkPXx0yn+63bsXVJvWbn7tZlQK8pws3fl1KpxPbdUaIl40cPxR1/n4b0/tlG6PLdV/fXtS7bG+hngB7fj/T7PkyNxb9LiYiIiDqezXsOobikTBTzdHWCrBkmB90QGVtd2HjT0TOXsGVvHCYOH6Tz/kns5JlzWL91l9bfYT39u2PqhNEGdd68tVhbWSJkYCD2x8RXxy4lXse6Lbvg0skRTg72cHSwg4W5mc5/WReXlCIm7jiOnEzQmnywPonXU5B4PQUers4ID+4PW2sr5OYX3ChizC9Ebn7hfwWMJfX+HZ6cloHflqxGUN8ADA8b1OITemkXN7JzI9HtCkvKsGDZDqRl5WstU0iBSd0kCHAAvjwsfm1bWZji7rEhBnMuZ0xYIE5euI7UzNzq2M6DJ9DHzwueLdjRueb7rYKTvRIRAQB2HjgqKgBXKOQYGdpfjxkREREREdGd8IxGB/bco/fh0tUk/PLXKlH8yrUUhE6ei0/eeAYvPj6nUV3/4k6cxaOvflrn8qXrd+Lt5+fCx8u9yXnTLdGx8dgdFasVDxkYiFFDwwzmJK++SCQSjBkejl//+rfOQTCNRoOc3Hzk5Obj3MUr1XGpVAp7O5vqAb+b/9rZ2qCktAwHDx/BsZMJWrPk3a6rlwfCBwfBy9O9+nfl190bV65eR3TsEVxPTq1zW10JgoD4Y6dw9vwljB4ejt4Bfs3+fMnOqdG5kbOUElW7mpSGHxatQuV/hc83WVlaYNb0Sejb0x8AcCrhPNLSM0XrjBsZAQ83l1bLtbkE+HfH4KBAxMYfr44lnL+ElLQMuLdAJ9nU9Cyo1eL3YDcXp2Y/DhGRITp4+CgKi4pFsfEjhzbb/isrK3HoyAlE7o/B7v0HEX/8NIyNFRgaOggjh4Zi9NAw+Pv68O8VIiIiIiLqgCRo/ok59PS3la5/00l0GKYW6h57qY2ft6dW7MzFawic/CT++PxV3DU6rMH7kkBe/f/T5xNx/0uf17rehz8uw8RRQ8R/+yrMG550TVI9Tviq44QuzTUhzNETZ7B55x6t/QX26oFJY0c0aky7xel0n3V8vGo5dmhQXxw9cQYlpbeKfE+eOY+TZ251UDM1MYGjgy2cHOxuTPbqYAdHe1uYm5vVew4nv6AQB+OO4/jpc1rn5W/n08UTgwf0QXJqBg4fO43yigqtdZLTMrBs7ZaG3NNbarnPgiAg7thpnL1wBWOGhaCnX7e674cOv6+bY8m3c7S3aeA+m/933YiNdTy2pv51WoQeJ5jS5fHW2+PVHHR4b5VIkFtQjO+XbkdOfrHWYh9bYHaAFI5mEvx2XI3yGjXR944Ph3kLTdTcEoxkMtw/OQJf/LEOGs2N54tGELBk4368MW9ai3xOCYKA7PwiUczURNHE56suz1NDPdevv+9HBjvRrKHS53u4RF/fUXV9XerwmOl6n410mJhCcutzY8u+o6JFw0L6w8KuU93bahr3d6b2sXX7zGz6tjpeAq635yga/be9iEp7IvkG0zRuIpZadqCnbfV5XkDX90J9fVfQ43uhToc11M8eIiIi3bC4sQOTyWT46bO3MDxsIOa99D4Ki0qql6lUKrz64bfYcyAOi7/7AE4O9RclpWZkY+qjr6OyskoUNzKSVRd4qdVqfPLDYvz59dvNe2c6GEEQsDf6EKJi47WWDQ0dhIjQgbxQuIGcOzkiImQg9h083KjtNBoNsnPytAr4ZDIpBAGiLms1+XbrgvDgILjXUpwkkUjg09ULPl29cD05FdGx8biceL3BeVlZWsDO1hr2draws7GGna0N7O1uFFxui9yvlW9pWTnWbd6J46cSMGH0MDg62Df4WHciCAKyc2sUNzbgfYSoI0jNyMaCv1ZrFTb6dOmMxx66FxbmZtWxqJg40TounZwwdsSQVsmzJUyfPBZnzl1EcUlpdSw6Nh73Tp/U7MdKSkkT3XZysINpC8/OTERkKDbv3Cu63a9PANx0KDTXaDQ4ffYCIvcfxO79MYiKjUdZWblonbKycmzdtQ9bd+0DALg6d8LoYWEYOTQUIyNC4eTYPN9DiYiIiIiIiGoKHdAT37zzFF7+5Beo1bfGb/IKijD18bfx9ANT8eWbT8DEuOHnD3PyCnDXvDdRWqZdnAUAR05dwLZ9hzF+WLDO+RNwKP44tu+J0ooHBfbG+FFDOS5aD4VCgeHhwdi4fU+d65RXVCApJR1JKemiuJmpyX8Tvd4odnRysK8ueszOyUP0oaM4c/7Sf4U82hd+SiQS+HfrivDg/nDpdKNzmbeXJwYP6IujpxIQE39CVHTZ3EpKy7BmcySOnz6P8SPC4dDMXRULCouhqlHQyc6NRDfkFpTgm7+3Ir+oVBRXSIG7uksQ7iGBVCJBeomAU9nibYN6+aC3r1frJdtMPJwdMDYsEFujjlXH0rLykHA5Gb26d2724+UXlaKkxncRD2eHZj8OEZGhUavV2LrnoCg2YWR4sx4j8XoqIg/EY8+BeOw/fBzlFZUY2DcAI8MHYnREMHr7+7StCViIiIiIiNo4FjcSZkwchQG9/TH7qTcRe/SUaNn2vTHoO3IW/v7+Q4wcMqjOfZRXVGLavNeRnpUjij/90AyYmRjjy1+WVseWrN2Bt559CN7s3tgkgiBg594Dos5XN42MCEFY8AA9ZGXYhoYFo6d/dySnpiM7Nw9Z2bnIzs3T6mbTELcPit9OIpEgwK8bwgcHoZNTw04md/ZwQ2cPN6SlZyI6Nh4Xr1yFWq2BpYU57O1sYGd748fe9ub/rSGXy2vdl5Mj8MTc+xAbfxz7Dx6GUimegehaUip+/nMZQgb2w5CQICgUisbd8RqKiktQVaNwi50biYCCohL8sGgVyivEM4gF+HXDvAdmil572Tl5OHvhsmi94UMGQybTdSYu/TEzNUVY8ABsi9xfHYs7ehJTJoyCqUnzzryanCoubvR0a/7ukEREhmrrzn2i2xNGD2v0Pq4lpVQXM+6OitGaob4+aRmZWPzvGiz+dw0AILB3D4yMCMPoYWEIHdQfJixIJyIiIiIiomb0/NzpGNjHD/c9/zGup2aKlv349zociD+Ff3+YDz/v+gsPqqqUuPupd3EtJUMUl0gkoq6CH37/F8YNHcTCOx1Fx8Zjd1SMVjxkYD+MGhrGx7eB+vb0w9kLl3H5alKjtisrr8D15DRcTxafczczNUF5RWWdnTmlUil6+XdD2MB+tU6AamysQEhQIAYG9sLx0+dwMO44ChoxNmtpYQ47G6sbk73aWP03XmqNS4nXsT/2KFQq8Vho4vUU/PzXSoQO7IvwQf3qHFNtrOwa58TMTE1gbmZWx9pEHUdRSTl+WLpdq7DR0Qx4qt+Nbo03RSeL30fMTY1x95iQVsmzJYwJC8Sxs1eRkXPr/SHqyNkWKW5MShdXhZqaKOBga9XsxyEiMjTxJ84iJ69AFJswIlSnfebkFWDPwSOIPBCP3QficTUpTWudyOg4REbH4fVPFsDR3hajwgdi5JBBGBU+EG4uTjodn4iIiIiovWNxIwEAvDxcsW/tQrz39a/47IdFokGIjKwcjLn3Kbz69EP44JUntE70C4KAea9+iviT50Tx4SH98X/vPIf8wiL8+NcalJXfmC1MrVbjkx//xh9fvtnyd6ydEQQBW3buxZETp7WWjRsxBIMG9G39pNoJB3s7ONQovqusrLzRnTH3RofGrJxcZOfkoai4pI69aJNKpejT0w9hwQNgb9e0WTpdXTph5rSJ0Gg0UKvVTR5sk8lkCAsegJ7+3bE9cj/OX0oULddoNDhw6AjOnLuIcSMj4Nuta5OOAwDZObmi28bGClhaWjR5f0TtgSAIWLZ+B4pKxIN4AX7d8PhD98LISPy1LLpGd15TUxMEBfZq8TxbWljwAOzYE13d4bayqgpxR08iIrTuSRSaombnRk+3Ts26fyIiQ3U58RrOX7oiik0cPbze7XLz8rH3wCFE7o/B7v0HcaWRF8LV5/ipszh+6iy+/OE3mJqaYMjgIIwaFoZRQ8PQ0787L1QkIiIiIiIinQ3uF4DjW3/H4298jVVb94mWnTx3BQMmPY4f3n8eD80YW+ffoYIg4Ln3v8f+wydF8eDAHnhwxlg8+db/VccOnziHnVHxGBMxsNnvS0cgCAL2Rh9CVGyc1rKIkIEYGhbM8wWNIJVKMXvGJFxLSkV6ZjaycnKR9d84aM1JURvi5th/TTKZDIE9/RA6MBC2Ntb17sfIyAhBgb3Qr7c/Tp+7hINxx6sLBi3MzWBnYw17W2vY2Vrf+L+dDexsrKFQ3DZeetu1Dc5ODujp1w3b9kTj4pXromOp1WpExR7FqbMXMX5EOLp7ezX6fteUnZsnus2ujUSAUqXCT8t3Iju/SBR3swCe7i+FlfGt9+5KlYC4NHFxY2g/f5ibNu+kqK3JSCbDiOBeWLr5Vsfhs1eSkZ1XBEe75i08TE4XT0Dv4eLIz0YiIgCbIw+Ibgf4dkUXT7dG7aOsvALRh0/c6M54MB7Hz1xs1PbZuflYtn4Hlq3fUZ3DyPCBGD0kGEOCA2FuZtqo/REREd2JRHLjp6PjY0Bk2FjcSNXkcjk+fv0ZDA8Nwv3PvoOM27owCoKAzxcswv7YI1j206fw8nCtXvb5gsVYtn6naF/end2w8pePIZcbwcnBDk/cPxX/99u/1cuXrNmGt599CF08XUENo9FosGFrJE4miItIJRIJJo0Zjn59AvSUWftlbGwMdzcXeLiLn6cVFZXIzsmtLna8+W/xbQVLRkYy9O/TEyGD+sPayrJZ8pFKpZBKpTrvx8baCrOmT8KFS4nYFrkfBYXiQYWCwiL8u2YTfLt1wbiRQ2Fj3fgT7Nk5NQfy7HgSnTq8w8cTcOaCuKjYp2tnzHtgplZhY5VSidj4Y6LY4KBAnbuqtgW2NtboHeCHE6fPVseiYuIwJGRgs71P5BcUIjVdPPu6p5tLs+ybiMjQbdm1T3S7k5MD+vftqbVeeXkFDhw6cqOYMeogjp86W+dM/LWRSCQI7N0DI4aEYviQYBQUFmPn3mhE7juI5NT0O25bXl6BHXuisWNPNADApZMTRkaEYNSwMIyMCIVzJ8cG50FERERERER0OxsrSyxf8C5G/NsfL3zwAyoqq6qXlZVX4JFXP0fkgSP4+aMXYWVprrX9z/9swG/LNolibs4OWPPLh3CwtcaXvy5H4m0dPD74/i+MHhLEMZJGEgQBO/dGIzb+uNaykRGhCAseoIesDJ9EIkGXzu7o0tm9OiYIAgoKi5Cdm4+s7Jwb/+bkIScvv1FFj3K5EQb0CUBIUF9YWmi/duojk8nQt6cf+vb0Q3l5BaRSKYyNmzYmYmtjhXunjseFy9ewbc8BFNboCFlQWIxla7fCr1sXjBseBmsdJmit2bmRxY1EwOb9x5GcKb5ewMUCeC5ICnO5+PMwPl1AhfrWbQmAsH7+rZBlyxrQ0wdrdx1C+W3fM6KPncW0kcHNepykGsWNni48d05EBABbIqNFtyeMDKt3G5VKhSOnzmP3f50ZY46eRlWVstlySriQiIQLifju9+VQKOQIHdAbI4cMwughgxDYy69ZrgkkIiIiIjJkLG4kLSPCB+FE5HLMfeFdbNtzULTs0NHTCBw1C799+Q7unjQKm3bux1ufLRCtY2lhhg1/fAE7m1sFUa88Phs//70W5RWVAACVSo1Pf/wbv33+esvfoXagtLQMWyP3IeH8JVFcKpVgyvhR6B3gp6fMOiYTE2N4uLtqFT2Wl1cgOzcPKpUKzk6OMGvjMyz5duuKrl4eiIqJQ0zcMajVGtHyC5euIvFaMiJCB2FwUCBkMlmD9601S6mDXR1rEnUMBYXFWLVljyhmZWmBxx68t9aCxbPnL6G0rFwUCx8c1KI5tqYhIUGi4sb0zGwkp6bD0715Jj04ePhodWdIADBWyNHZ3blZ9k1EZOi27Ngruj1u5FBIpVKo1WocO5mAyP0HsXt/DA7GHUXlbRdeNIR3F0+MiAjFiCGDMSwsWKsz+sypEyAIAi5cSsSufQexa1809h44jNLSsjvuNz0zC0tWrseSlesBAL0D/P7r6hiK8OAgmBrwLN5ERERERETU+iQSCR67bxJCB/TErGffR8LFa6Ll/27cjcMnzuHf7+cjqM+tMbg9Mcfw/Pvfi9Y1MVZg/W8fw8XJHgDwxlOzMe/1L6uXxx5LQOSBoxgVzmK8hhIEAVt27sWRE6e1lo0bEYFBA/q2flLtmEQiga2NNWxtrNHdu3N1XKPR3Cp6zMlDVnYusnPzkZOXD5XqViWSiYkCAwN7Ibh/H5g10zma5jjXI5FI4NetC7p2dsf+2COIPXJSNG4AAOcvXcWVa8kYOngAgvv3btRY6E0sbiQSu5qahd2HE0QxB1Pgmf7ahY2CICA6WTyhXs9unrC3aZ7Jo/VJITdCcN/u2Hv4THXs0IkLmBgxAAp581yqV1GlRGJyhijm6criRiKi5NQMnDwrvsZywgjt4kZBEHDhynVERsdhd3Qc9sYcQVFxqdZ6d+Job4sRoQMwIjwINlZWiDwQh8ioOFy5nnLH7aqqlNgbcxR7Y47irc9+gr2tNUaGD8SoiGCMCh8IDzde30JEREREHQ+LG6lWTg522PT3d/h24TK88cn3olkZC4tKMPPx17Bxxz5s2LFf1L1DIpFg2Q/vo0f3LqL9dXK0w+NzpuDb31dUxxav2oK3nn0QnT09Wv4OGaDi4hKcu3gFZy9cxvWUVK0uKTKZFDMmjYW/r4+eMqSaTE1Nmq0wp7XI5XKMiAhF7wA/bNm5F9eSUkXLlUoVIvcdxMkz5zBh9DB4ebrXsSex2jo3EnVUgiBg2Yad1QX+N903YzIszM1q3eZqjROdPl07o5OjQ4vl2Np8fbrC3s4WuXm3Bv2vJaU0y3uoWq3GgUNHRLGBgT1h3A66XhIR6aq4uAT7Y+JEsdLSUkx/4CnsPXBIq6N3fRzsbTFiSAhGRIRgxJAQdOlc/992EokEft294dfdG88+9gCqqqpw6MgJ7Nx7AJH7DiL++Kl6O0SeSjiPUwnn8fWC32FsrEB4cBBGDQvD6GFh6NXDlzObEhERERERUYMEdO+CuA2/4sWPfsSvSzeKliUmpSF0xtP45JV5ePHRe3A1OR33PP2e1kSRi756Hf17+VbffmDaGHy8YAmupdwqNvjg+8UYGdYf7N1Yv4qKSmzfHYUTZ86K4hKJBJPGDEe/Pj31lFnHI5VKYWdrAztbG/j63Br712g0yC8oQnZuHuRGRnB3db7VYbGeczr6oFDIMSpiMPr06I4tkdG4npImWq5UqrAr6hBOnr2ICSPC0dmj4eMUgiDUUtzIMVHquKqUKizZdEB0ftdIAjweKIW1sfanYGrJjZ/bhQ8IaOk0W014vx6i4sbS8kokXE5CoH/XZtl//OlLqLito5gEgLcHi2GIiLbsFjfzsLWxwuD+vQAAaRnZNzozRsdh94F4pGZkNWrfZqYmiAgOxIiwIIwMD0JPX+9b45ISKaZPGA4ASLyeil3Rh7Fr/2HsPhiPwqKSO+wVyM0vxIqNu7Bi4y4AgJ+PV3VXx4jB/ZrUGZ2IiIiIyNCwuJHqJJVK8eLjczAkuB/uffJ1XLkmLrRYunab1jafvv4kJowIrXV/rzw+G78sWYeK/7p/qFRqfPbTP/j5szeaP3kDVVhUjHMXLuPsxctITk2v86JeI5kMM6dOQDdvr9ZNkNotRwd7PHjvdJw+ewE7dkdpdYzLzsnD4mVr0DvAD6OHhcHiDidNbgzksXMj0U1HT5/HmQuJolhQv9537LqblCoeXPfu0rmONQ2TRCJBVy8PUXFjUo0LCprq5JlzKCoWnxgOHxTYLPsmIjJ0u/YdhFKpFMVWbdD+u64uZmamGDI4qLqYsXeAn86FhAqFAkNCBmJIyEB89NaLyMsvwO6omBudHfcewPXk1DtuX1lZhcj9BxG5/yBee+9zODnaY1REKEYOvdHZ0dWlk075ERERERERUftmamKMnz96ESND++PR175A4W2dOlQqNV799BfsPngUyenZyCsQTwr05tNzMHPicFFMLjfCG0/NweNvflUdO3jkDPbEHMOIUaNb9s4YqPLyCpy/dGPC18TryVCr1aLlUqkUU8aPuuM5dWo9UqkU9nY2sLez0XcqjeLkaI+HZt2FU2cvYue+GK2x0KycPCxasQF9A3wxKiIY5ma1T055u8KiYihVKlGMnRupI9sSdRyZuYWi2DhvCVwsai/vv1Ygvh7G1soc/t4Nm2zZEHRysEG3zi64dD29OnYtNatZihsFQUD0EfFEAD27d4aNlYXO+yYiMnRbdx8Q3fZw6YSXPvgWu6PjcPbi1UbtSyaTYVBgD4wIG4iRYUEYFBgAhUJe73ZdO7vh8c7T8PicaVCpVDhy8hx2Rh1GZPRhxB49o/U3T03nL1/D+cvXsODPFTAykiFkQG+MDB+E0RHB6N/br0kdx4mIiIiI2joWN1K9BvTpgaM7luHpNz6ttaDxpjlTx+CVJ2bXudylkwMmjgjF6q17q2ObdkV3+OLGgsIinL1wGWcvXEJKWka968uN5Lh3+gR09fJsheyoI5FIJOgd4Ifu3l2wOyoGR46f1iqwPZVwHhevXMWY4eEI7F37rIklJaWoqNGhztHBvsXyJmrLBEHAzihxhywrSwvcM2X8HbdJTkkXxQytK2xDeLq7Iv7YqerbzVXcWLMjmbeXO9xdnJpl30REhm7zzr31r3QbqVSKgf16Y0REKEZGhCB4QF8YGxu3UHY32Nna4O67xuPuu8ZDEARcTryOnXujEbnvIPZEx6K4pPSO22dl52Lp6o1YuvpGx40Av27VXR2HDB4IMzPTFs2fiIiIiIiIDNP0cRHo36s7Zj//EWKPJYiW7YiK11p/yugwfPDiw7Xu68HpN7o3JqVlVsc+/P5vFjfeprS0DOcvJeLshUu4mpQCjUZT63oymQwzJo2Fv69PK2dI7ZFEIkGfAF9079oZu6MP4+ips1pjoScSLuDClWsYETYI/fv0gERSd8/Vml0bTYwVsDCvvyiSqD1KzsjF7sPiz8/OVsBIr7pfQ0niOQPQ1cMZ0ju85gyRt4ezqLgxKT2nWfabmJKJ1CzxhNPhQexuTERUVl6ByAPiv99OnbuEU+cuNXgfAd27YnjYAIwMC0JEcD9YWerWNdHIyAjB/XshuH8vzP/foygqLsG+2GPYuf9GsePFxKQ7bq9SqRF16DiiDh3H/C9/ga2NFUaEBmFUxCCMGjIIXp68hpSIiIiI2gcWN1KDWFla4O8fPsLIIcF4+o1PUVZeIVo+sG8P/Pb563c8uX/01Hls2BUtinV2d26RfNu6vPyC/woaLyMtI7P+DQAo5HJ09+mCiJCB7IJHLcrExBgTRg9DYK8e2LxjD9IyskTLKyoqsWFrJEyMjWsdTK7ZtVEuN4K1lWWL5kzUVl1LTkdKuvg1dPdd4+84429uXj7KysUzBnu6tcPixhr3KS0jC0qlEnJ5/bPc1SXh/CVcunJNFBsyqF+T90dE1N6cPV//wJ1/d2+MiAjFiCGDMTQsWK/f4yQSCbp5e6GbtxeefvR+KJVKHD56Erv2HcCuvQcQd+xUnRc/3pRw/hISzl/Ctz8vgkIhR9igARg5NBSjh4Whb68eOneeJCIiIiIiovbDy90F+5Z/h/e+XYzPfl6qVfR0Uy/frvj7/96s829KhUKOh2eOx3vfLKqORcWdRH5BIWxtrFskd0NQUlKKcxdvdGi8lpxS5+N7k5HMCDOnTkA3b6/WSZA6DFNTE0wcHYG+PX2xJTIa6ZnZouXlFZXYHBmF42fOY8rYYXWOzdcsbnS0t73j9RJE7dnuw2dE7+tGEmBOTylk0rpfE8lF4s8BTxeHFstPXzxq3KfkjBwIgqDze0VUja6NDrZW8Pf20GmfRETtwd6DR7Qm5K+Pm7MTRoYHYUTYAAwPGQBXZ8cWyu4GK0sLTB49BJNHDwEAXEtOw67oOERGxSHyQBzyC4ruuH1+QRFWb9mN1Vt2AwC6dfXEyPBgjI4IxrDQIFhZsosvEVFHJP3vp6PjY0Bk2FjcSA0mkUjw4D2TENyvF+598g2cSLgAAPBwdcba3z6FiUndHTxKy8ox+7n3oFSqRPv79PUnWzzvtiInN6+6oDEjK7v+DQAYKxTw9emKHr4+8O7ieVvBx50H+4iag6tLJzz6wEwcPXEGu6NitE7+XE9Orb24MUdc3Ohob8eBPOqw9h8+Lrptb2uDwN497rhNzQ6G5mZmsLNtfxe8eLi5QCKRVA90ajQapKZnwsvTvUn7Ky+vwNJVG0QxSwsz9O3ZXedciYjaiwGBvRB37KQo5tLJCSMjQqoLGt1c2+4ENHK5HGHBAxAWPADvv/4CCgqLsCcqFrv2HcSufdFIvJZ8x+2rqpTYEx2LPdGxePPDr+Bgb4uREaEYNTQMo4aGwt3NpZXuCREREREREbVVcrkRPn7lUQwPCcT9//sYGdniMQ8HO2usX/jxHbuz5eQV4NelG0UxGysLmJmatkjObVlRUTHOXriEsxcuIyk1rd6CRuBGUaN3F08MDR0EF2enVsiSOip3V2fMmzMd8cdPY8+BeFRWVYmWp2Zk4a9Vm/Dsw/fC2Fihtb12cSMnKKaOqbi0HMfPXRPFRneVwMWi7msElBoBaSXimKdLyxaT6EPN+1ReUYWc/GI42lk1eZ/JGTk4dvaKKBY+IKDddb0kImqK0+cv17uOtZUFhoUMwIjwIIwMG4ju3p1vXNemUbdChtq8PFwxb/ZUzJs9FWq1GsdOn6/u6ngw/iRUqjvndSkxCZcSk/DzXyshk8kQ3K8XRkUEY9SQYAT1DYCRES8RJyIiIiLD0Ea+uUqAZj3JouO+JDrUbQt37hpx5+PqmHcDBoPqJG34U8G3uw9ity7F8vXbkJNXgAfvmQwHuzsXXbz40de4mJgkir3+7MMYEhYKKMyblDIAQCpr+rZAi/5RKggCsnPyqgfssnJyG7SdibEx/Lp5o4evD7p6edTxB2bHPCnJArnWJoFUKkNQvz5Iy8jC8VMJoqXeXTqjtudizc6Njg72ta5HZNAa8H5UUlqGY6cviGLhg4Pq7RBVc4ZgT3eXdvn+Z2JiDCcHe2Rm51TH0jOzm1zcuHrTdhQUimewmzRqCOQ8Uds0+vxeqtNnho4TQOhyv3W6z4Au91ufbxENuSCtLrq/t+nrjhvuRCNfvPcanJ0ckXg9GYG9emBkRAj8unu3+OdMS+3f1sYa0yePxfTJYwEAV65ex659B7FzbzT2RMWisKj4jtvn5OZj+drNWL52MwDAv7sPRg0Nxejh4YgIGQgLC3Po63mm+2PW9OepLq/rGzvQ4byITq8vPb4ZGuz7mSHT43O8mb+rCDq9ZoiIiIiopYwI7Y8TW//A3Fc+w7Z9hwEAxgo5Vv34Prp41D05jiAImPf6l0jPEo8LvvvCQ7UWR7VHBYVFOHfhMs5euITk1HQIDfj+biQzQjdvL/Tw9UF3by8YG9c9qS5Rc5JKpRjUrzd6dPfGzn2xOH3+kmh5SWkZsnPz4e7aSWvb2jo3EnVEsScvQaW+dX7DSAoM8bjz+ZP0EkBd4+PBw7n9dW60tTKHhZkJSsoqqmNJ6dlNLm5UqdVYsnE/NJpbD57cSIbgvn4650pE1B4E9vTViikUcoQO6IPhYUEYGT4Q/Xv7tdmCP5lMhqC+AQjqG4C3nn8YxSWl2B97DLuiD2PX/sM4f/naHbdXq9U4GH8CB+NP4L2vfoG1lQWGhw7EqIhgjI4YjK6dm3Y9DhERERFRa2ib39KpzTM2VuDBmXfdCtzhQqz12/Zg4T9rRLGgvgF47+UnWio9vREEAZlZOf91aLyEnLz8+jcCYGpiAv/uNwoau3T2gEymY9EmUTO6npyqVdjYzdsLPl29al1fq3OjA2cppY4p5uhpqNS3iuiNjIwQMqhfvdtVVopnBraytGz23NoKK0sLUXFjVY1ZkRsq4fwlxMYdE8X8fLwQGtRHp/yIiNobMzNTvP3y0/pOo8V4d+kM7y6d8cTc+6BSqRB/7FR1seOhIyegVt95cptzFy/j3MXL+P63vyCXyxEysB9GDwvDqKFh8PXpiiqlEpWVVaisqkJVlRKVVVWorKxClbJKHBetU/nfOnVtq0RlZeWt21rLam5TiSqlEhqNgKDAXnjn5WcxZsSQVnqEiYiIiIhIdwLa1KQ5Eh3Go3Se40OHyWY1yqZvK2/4pKtOrhbYtOQHbNixHycSLmL6+OHo3SvgjtssXLIaG3YdFMXGDgvFc0/Og6QRx9aiy+MFABqVDhvX/5zNyy/4r6DxMlLTMxu0V4VcXl3Q2K2rFxSK2oo/dXi96DyxiZ5eq/rM21CP3Qx5W1qYYdLoIbiWkoriktLqJSbGxujkaIua900QhBsTvt52bEc760ZO+qTHzwOdJ9ox0NeHLvdb5+eZLtruc0Wj0SD62HlRrL+zBBaKO39RyCgR3ydHOyuYmrS/SQAkEgk8XRxx9kpydSw9u2HXEdVmx4ETSM0UT6AwIrg3LEzkur+uhY44AZs+J9vT12emru//epykVq/3Wwe6TI6o698AutCpwUfzpdFYoyOC8ff372Pbnhi4uThhZPhAhA3sCzNTk/o3bkRzkFrp9Pd17b9rSysFJo4ZgYljRgAAklMzsCsqFpFRh7Br/yHk5hfccbeFRSVYt20P1m3bAwDo2tkdo4aGYFTEYAwPGwgbayv9Ps90uXxdl9+Xrg1RVBX1r1PnsXX521yPdP4ersv2urwf6Xgdtr4aJelKX5MR13ZcTvJKRESNwOJGalFpGVmY99IHopi5mSn++fETyOVyPWXVvARBQFp6Js5euIxzF68gr6CgQduZm5lVFzR29nBjQSO1SSqVCpu27xbFFAo5JoweXmc3Ga3Ojfb2LZYfUVsWf+Ks6Ha/PgGwMK//4hWlSnwiSy5vv1/Xat63KmXjL0oqKi7B0lUbRDETYwXmTB/XLjteEhFRwxgZGWHwwH4YPLAf5r/6LAqLirHvwCHs3HsAu/YdwKUr1+64vVKpxP6Dh7H/4GG89dHXrZN0Ix08fBRj734IY4YPwVcfvIGePbRnoyUiIiIiIiLdSKVSTB03DFPHDat33fOXruJ/878QxRztbfHndx+2y3OVuXn51RO+pmdkN2gbY4UC3b27oIevD3y6dm4348XUPuw9GC8qbASA0RGDa32eFpeUorLGhI3s3EgdUcKVNOQVil834fV0bQSAihrX9VtZmDVnWm2KlYWp6HZlE8ZDASA5IwfbD4gne3V1tMXY8Pon1yUi6igkEgnmTB+POdPH6zuVFuHh5oyH752Kh++dCo1Gg+Onz2NX9CHs2heLg/HHUVV158+YxOsp+PWvlfj1r5WQSqUYGNgTo4aGYlTEYAT28odKra5jAlZlHRO4/reOUonKyjrWqapCVaWy9kldb9//betXVlZBrdGgb4AvXnvuUUwcPbRd/k1NRERERGLt92p50juNRoOHnn9Ha4aY7z56Dd26dtZPUs1EEAQkp6ZVFzQWFBY1aDtLC3P4d/dBD18feLq7QirV58w3RPU7cOgIcmoUKw4PD7kxc1MtSkvLUFZWLoqxcyN1RJVVVUjLzBHFQoIaNrCkqlHcaGTUfovfjYzEX0VVqsbNjlZWXo4FC//W+hyeNn447Gysdc6PiIjaD2srS9w1fhTuGj8KAHAtKQW79h7Azr0HsDsqBvkFhXrOsOl27InCrn0H8Oj9M/HBGy+gk5OjvlMiIiIiIiLqcKqqlJj91GsoLxd3cPjjmw/g7OSgp6yaX3ZO7n8FjZeRmZVT/wa40fnOr5s3/Lt7w7uLp9Z5YaK2IC0jC4eOnRLFvDxcEdjLr9b1s3PFndcUCjmsLC1aLD+itio+4arotocV0Ln2SwlElDUauMjb9Xio+L41djwUACqrlFiycT80mluddKQSCe6/a2i7fuyIiKhuUqkU/fv0QP/Annj9uUdRWlqGqENHsWt/LHbti0XChct33F6j0eDQ0VM4dPQUPvz651bKunFi4k/grvufwbCwgfjq/VcR2Mtf3ykREbVZEoluDaPbCz4GRIaNIwfUYr79bSkiow6LYtMnjMTcWXfpKSPdCYKAoydOIyomDoVFDbv41crSAj18fdDDtxs83Fw4iwwZjOycXETHxotirs6dMLB/n7q3qVEIaWRkVGchJFF7lpKeDUG4NbgkkUjg5enWoG2NZLoPcBkKXQo5y8sr8OPv/yAlLUMU9/PxQmhQ3e9TREREAODl6Y55D87CvAdnQa1W4+iJM9i5Nxq79h1ETNwxrc+otk6j0eC3v/7FsjUb8frzT+DFpx6GqamJvtMiIiIiIiLqMN75fAGOnz4vij350ExMHB2hp4yahyAIyMrKwdkLl3D2wmVk5+TVvxEAU1MT+Hf3Rg9fH3h5ukMmY+EFtV0ajQabdkaJxnWMZEaYNCqizrH9msWNjva2vA6AOqTr6eJC98Gukga9FlQ1ihtrjo+2J3Idx36VKjUWrtqF1MxcUXx0aF94unCiOyIiusHc3AzjRoRj3IhwAEBqeiYiow4hcn8sdu2PRVYD/5Zri/YeiMOAkXfjwZl34cM3noObSyd9p0RERERELYDFjdQiTpw5jzc//V4Uc3Nxwq9fvmPQJ/VjDh/Frn3R9a5nY211o6Cxuw/cXJ0N+j5TxyQIAjZt3w21+taJdalUisnjRt6x42h2jviEuoO9LTuUUoeUlCouuHNxdoJCoWjQtnK5XHRbqTSs4orGqHnf5EbyOtYUKy4pxYKFfyM5NV0Ud7CzwYN3T+TnLhERNYpMJsPA/n0wsH8fvP3yMyguLsH+mLgbxY57D+L8pSu1bieVSmFsrICxQgFjYwUUcvl/t42hUMhriSvEcUVt296+jnGd2548cw7vfPoN0jOyRDmVlJTi7Y+/xi+LluLT+a/gvhmT+X2ciIiIiIiohe05cBhf/bRYFPPv1hVfvfuSfhJqJmfOXsDe6Bjk5jbsIlhzM1P4+/qgh68POnu4VRc03l4wRtQWHTp6CulZ2aLYkOB+sLezqXMbreJGO9uWSI2oTSsrr0ROQYko1tWmYWN0RjVOWarVmtpXbAdUNe5bYwr+1WoNFq/bg3OJKaK4q6Mtxob3a5b8iIiofXJz6YQHZ96FB2feBY1Gg1NnL/7X1TEG0YePobKySt8pNoogCFi8fD1WbtyBl5+ai1eefADm5mb6TouIiIiImhGLG6nZlZWVY/ZTb6KqSlkdk0gk+PuHj2Fna63HzHSTnpGFPdExdS63s7H5r0OjD1ycnVhYQQbt2MkzSEpJE8UGBwXCudOdZ/6r2bnR0d6u2XMjMgQ1ixs93VwbvK1CIS7wKy4pqWNNw1dcUiq6bayov7jxenIqFi1bjaxscTG1tZUFnntkFqytLJo1RyIi6ngsLS0wccxwTBwzHIAEObl5KCsv/6/A8FZBoj67TgwI7I1Z0ybhyx9+w5cLFqKsrFy0PCUtA/c/8RK++3Uxvv7wTQwJGainTImIiIiIiNq33LwCPPjsW6ICPoVCjqU/fwZTUxM9Zqab4yfPYMPWXfWuZ2Fuhh5+Pujh2w2e7q6cYIcMTn5BEfYejBfFHO1tETqw7x23q61zI1FHk5QhvjbASAq4NHCYTl7j46JK1Y4ne61x3+Tyhp1XrlKq8PvqXUi4nCyKm5sa4+HpIyE3ar/dLomIqHlJpVL07emHvj398MrTc1FeXoHow8eqix1Pnb1Y57Yymey/iVjltU/KWv3/25bJ5VAYay9TyO80Oax42aXE65j/+Q9IrnH9VVlZOT746icsXLIKH772DB6cOVmvY7ZERERE1HxY3EjN7pUPv8G5S4mi2MtPPYhhoUF6ykh3SqUSazdtF3WxAwAHO1v08O2GHr4+6OTkwIJGaheKi0uwa99BUczWxhpDw4Lr3TY7p0Zxo4N9s+ZGZCiSUjNFtz3dG17c6OwkLiJOTk2HIAjt7jOmsrIKmdk5otidCqiVKhW27tqHXXsPQKMRz3BqZWmO5x6eBYc7zKJMRETUVA5tdMIOc3MzvPf6C5j3wCy888n/YfG/a7S6YRw5fhoRE+/F1Imj8fm7r6Kbdxc9ZUtERERERNT+CIKAJ175AKnpWaL4J28+j749/fSUle7y8guwLXJfncutLC1uTPjq1w0ebi7t7tw1dRyCIGBLZJSo8EgikWDy6KF3vEBaEAQWNxIBSMoQT0TqZgHIpA37TFDUeImVlJbXvmI7UFJWIbqtMKr/Ur2c/CL8tX4vElPEY87GCjmeunccXBz5nkNERE1namqC0UNDMHpoCPDeK8jNK0B+QWGtRYf6KhwMGRiIe+4ai29+/RuffbcQJaVlouXpmdl49MV38cMfy/DVey9hRHj91zUSERERUdvG4kZqVpt3ReHnxStFsX69/PHhq0/rKaPmsWvvAWTnik/Mhg0agBERIRywo3ZnW+R+VFRUimITxwyHXF5/RzWt4sY2eiE4UUsSBAGZOeLPDA93lwZv7+khLoQsLilFfmER7GwMt/txbVLS0kUFGFKpFG6uzrWuez05FX8vX4f0zCytZXY2VnjukXvh5MBBPCIi6pjcXJ3x54Iv8NzjD+Gldz7GnqhYrXXWbd6JzTv24ulH5uCdV56Bna1N6ydKRERERETUziz6dz3WbIkUxUZFDMYLj83RU0a602g0WLtpG6qqlKK4jbXVjYJGXx+4uTpzfJTahTPnL+PyNXFHtAF9esDDrfaxiptKy8pRXiEuVmJxI3VENTs3elo1/LPB2VwC4NY4YVZuISqqlDBR1H9NgqFJThdP9trJwabOdTWCgANHz2Fd5CFUKcUdHxVyIzwxcwy83JxaIk0iIurA7O1sYN8GJxM3NTXBmy88hkfum4b5ny/AH0vXaE2GfjLhAkbd/RgmjhqCL+a/BL9unOiViIiIyFBJ9Z0AtR8ZWTl4+IV3RTFTExMs/elTKAz4BOSlK1cRd+yEKObSyQnDwoM5cEftzoVLiTh74bIo1jvAD95dOte7bXl5BUpKS0UxRwcWN1LHo1KpodGIuyZZWpg3eHtHezuYmpiIYkkpac2SW1tyvcZ9cu7kCEWNIurU9EwsW70RX/6wsNbCRg/XTnjpiTksbCQiIgLQt1cPRK77B5v+XQi/bt5ay5VKJb79ZRF8+g/HNz/9iaqqKj1kSURERERE1D5cSryO59/+TBSzt7PBou8+hFRquEPwUQcPIyU1QxQL7N0Dzz/xEEYPD4c7OzVSO1FWVo7tew+KYpYW5hgRNqjebWt2bZTLjWBjbdms+REZgszcQtFtD6uGb+tqCdze5FEAkJKRU+f6hqqguBSFJeJOU51dHGtdNye/CD/8swUrth3QKmw0NVHg2TkT0N3LtdZtiYiI2rNOTg749ev3cGLvWowdHlbrOpt3RaHX0Gl4+vWPtZozEBF1BFL+VP8QkeHia5iahUajwdzn5yMnT3wi/5sPXoavj5d+kmoGpaVl2LB1lygmN5Jj+uRxkMlkesqKqGVUVlZiy869opiZmSnGDB/SoO1rdjeVyWSwbWed5ogaQqlSacXkRg1vli2RSODhJu70mNwOixtrFmx6ut0YjFOpVDhy/DT+78c/8PHXP+LAoSNaM6/JZFJMHBmOV596ALbWjRgpJSIiauckEgkmjhmBUwe2YMEX78Ghlk7q+QWFePHtjxEQMhZrN+0QdVImIiIiIiKi+imVSsx+8nWUlpWL4r//3/twdTbcbkopqemIiokTxexsbTBuZAQLGqnd2bk/Vus1PH54GExMjOvdNjtXfLG0g50NXyPUIVXWKMCzUDT8daCQSeBSY27YpPT2V9xYs2ujiUIOR3vxNRQFxaXYsv8IPv51NS5e0x4TtrW2wP8emISu7p1aNFciIqK2rqd/N2xd/iu2Lf8VPf18tJar1Wr8vHgFug2eiC8W/ImKiko9ZElERERETcXiRmoWC/5cjh37YkSxu8YMxbw50/WUke4EQcCm7ZFanehGDw+v9QJRIkO3JyoWRcUlotiY4UNgbm7WoO1rznpkb2fDImDqkNRqtVbMqJGvBU938aybideSdMqprREEAVevJ4tiarUa/67ZhLc++hp/Ll2Fy1ev17qth2snvPb0Qxg/IpTvMURERHWQy+V4+tH7cfnoHrz63GNQKBRa61xOvI7pDz6FiIn3Iv7YKT1kSUREREREZJje++pnHDmZIIo99sDduGvsMD1lpLuqqiqs3bRdNNGcVCrFtElja/2bksiQXU1KwYmEC6KYf7eu8O/etUHb1+zc6Ghv22y5ERkSlUo8Jipv5LCdh5W4GDIpPVvXlNqcmvfJ3dkBUokEgiDg4rU0/L56F975bhm2Rh3T6tYIAKH9/PDW4zPg1sm+tVImIiJq88YMD8OxyJX49av5cHLQvo63qLgEr3/0LfzD7sLy9ds40SsRERGRgWh4GyGDouOseDrNqqfHelFdDq3DfT599iJe++hbUcylkyMWfvMhJDJ5Aw7dNgsTjp9KwPlLV0Sx7t5dMSCwt54yMnyGO2OloebdcCmp6YircUFzVy9P9A7wa/A+ahY3OtrzBHurMtjXl6Gq+/GWybS/XqlqKXi8Ey9Pd9Hti1euITsnD461nJQzRJcSr2m9Z8Qfv3NRhUwmxbhhoRgzNJhFjYZAl/cknU4s6/peyJPajaXb9zt+dlHD6DLgZLh/g+j2fnTzMbOytMBn776Kxx+6F29++BVWrNuitW50bDwGjpyK2TMm4+N3XtaaZKHRJDqcnBAa951JvK2m/nXuRG+fXYBO74cd9X1Yp8dcx9+XLuexan2e8vsHERERkSHZH3MEn33/hyjm6+OFr999SU8ZNY/tkfuRl18gikWEDoK7mwug0S62IDJUSqUSm3buF8WMFQqMGx7a4H2wuJHoBpVafJ7DqJGnmjysgEO3NSq8mpIFQRAM+JyutqspWaLbpiYKbN53BMfPJSIjp6DO7WytzDF7UgT8u7rXuQ4REVFHZmRkhHlzZmDWlHH4fMGf+L9f/tbq1Hg9JQ33PfEavl+4FF+99zJCgvrqJ1kiIiIiapB2WtxIraW8vAKzn3wVlZVVovjiHz6GgwGfxM/Ny8e2XftEMXMzM0weN7JdnUglAm50S9u0Y7foonEjIyNMHDO8Uc/37NwaxY3tpAiLqLHkcu2vV0pV4y7+6OnfDaYmJiivqABwo0Ah+lA8pk0c0yw56ltUTHyD17WyMEdoUB+EDuwLOxurFsyKiIio/erS2QP//v4dnnv8Qbz09ic4dOSE1jpLV2/Ems078MITc/H684/Dysqy9RMlIiIiImrvBI3uE3M0J31OQFrLJHEN37b+yVXrZGTc5E3zCwrxwLNvicZT5HI5lv7yFcytrOvfgU6Twej4vLnD9ucuXMaxk6dFMXdXZ4QPHgAImmbocqHD9vqc2ESfx9aFoT5mrZR31KGjyCsoFMVGhg+ClYV5g3PIzsm/7ZACHO1sGpVDNb1+Hujzd63D/dbr+5Eef1863e+Wy9tIJr6WQNXIQ3lZS3D77yQnvwiXk9LRrbOOk6+1EbkFxTifmCKKnb54HacvXr/jdqGBvpg6MgimxgpAaMQYs6DHa5n0+R1Hl2PrNPmbPicsM9Bj6/oerq/3YYPt+KZrcxA9vq51eo7rMpmkjn8f6/KY6XI9qk7vg82wvS50eX1Jb/y+LK2s8NGbL+DxB2fi7U++x5JVG7VWPXT0FMImPYC7J4/Bp2//D107uzX9uAAgMWv6turK+tepi0aHSWL1TZfcdfqo1/X5rcvr2kDfR5v9b3ND/RwlIiJ90OM3UzJ0giDg5fe+xJlzl0Tx/z3xIEZFhOgpK92p1Wqs3bQdSpVSFJ88biQsLMz1lBVRy4mNP4bMrBxRbGjYINjZ2jRqP9k5uaLbjvYsbqSOyUgmg0wm/opVVFTcqH0oFAoMDgoUxWLjjqFKqaxjC8NRWFSME6fP1rtety4eeOTeu/DRa09h0ughLGwkIiJqBoOD+uHg9lVY/sd36NLZQ2t5RUUlPvv2F3QfOBK/Lv4XqkZO0EBERERERNReCYKAJ1/5EMmp6aL4R28+j359AvSUle6Ki0uwaXukKKaQyzFt0lhIpbyUgNqXzOxcHIw/IYp5uDpjQJ8eDd5HWVk5SsvKRTF2bqSOSm4kLgJRNvIabE8roFON2oCoI/WPIRqKA8fONfhSboXcCKF9u+ONeXfhvgmhNwobiYiIqME83Fzw14+fIm7nCgwZPKDWdVZt3IEeYZPwyvtfo6CwqJUzJCIiIqL6cESCmqS0tAyzn3gFPy9aLor3CfDFJ28+r6esmkdUzGGkpmeIYgP69oZvN289ZUTUcvLyC7DvwGFRrJOTAwYH9WvUfiorK1FUXCKKsXMjdVQSiQTOjvaiWFKNC14aInxwkOh2aVk5jp1M0Cm3tuDg4aPQaLRHN+1trRHY0xd3jYnA2y88gv89Nhv9e/vDyEiPs8cTERG1QxKJBPdMmYCzsdvxxfuvw7qWDo1Z2bl48qV30HfIRGzfvV8PWRIREREREbUtfy5bh5Ubd4hiw8OD8dJTc/WUke4EQcCGrbtQVl4hio8bNbTRE2AStXUajQabdu4XjU/IpDJMGh0BSSM65WTn5Ytuy2Qy2HJyRuqgjOXiTspFlY3ryiKRSBDmIX79nTh/FYXFZTrnpm9KlRoxx8/Xu14ne2vMGD0IHz93D+6bEAr3Tvb1bkNERER1G9C3J/auX4y1i7+HTxdPreVVVUp8/dNidAuegAV/LIOyHUwyT0QEAFIJf27+EJHhYnEjNdqVq0kIGT8by9dtE8WNjGRY9N3HMDbgGcSSU9IQFRMnitnb2WL08HA9ZUTUcgRBwKbtu0XdWCQSCSaPHQmZrHHFRDm54oE8qVTKgW/q0DzdnEW3k1LSGr2PTk4O8OvWVRTbExUDtVqtU276VFFRiejYeFEsoHtXfPnO8/jw1Scxb/ZUjBk6GK6dHPWUIRERUcdhbGyMl595FJeO7MbTj95f698AZy9cxvh7HsHYGQ/h9NkLesiSiIiIiIhIvzQaDd794kfMe/FdUdzG2gqLF3xq0N0N44+dxOWr10Ux/+4+6Nur4V3siAzFkZMJSEnPFMVCB/aFUyMna82uMSbqYGdj0O8DRLpwdhAX9iYXN34fg1wlkN/2EtJohAYVBbZ1J85dRUlZhVbc1FiB7p2dMSK4J56fPRbvPD4Vw4J6wMzEWA9ZEhERtU8SiQRTxo/AmegN+Oaj12udjCQ3rwDPvfkpeg+dho3b90IQGjdJAxERERE1P55lpUbZtjsaQaNn4vS5i1rLxo8cAmNjeS1bGYbKykqs3bxd9IeKVCrF9EnjoFAYbsEmUV1OnjmHq9eTRbGB/frAzdW5ji3qlp2bJ7ptZ2sDIyMjnfIjMmQ1ixuTm1DcCABDQgaKbqekZSBy/8Em56Vv67bsRGGReGRz0ughMDcz1VNGRERE5GBvhx8+fxenD2zFpLHDa11n594DCIyYhMf+9xYyMrNbOUMiIiIiIiL9KCgswl33P4sPv/5Fa9knb/0P7k0YT2krsnNysXNvtChmaWGOSWNHNKqLHZEhKCwqRmTUYVHM3tYGQ4L7NXpfNYsbHe1tdcqNyJB5dhIXBycVNb4owEwuQZCL+HMn+uhZVFQZbhcljUaDPYdPiWLODtZ498np+OKl+/D8nHGYNiII3b1c+JlLRETUghQKBZ5/7H5cOrwNLzz+AORy7WsZL1y+hikPPocR0x7BsVNn9ZAlEREREd3E4kZqEI1Ggw+//hkT73sSBYVFomXGCgXunToefXv64fLV5Dr20PZt370f+QWFotjQ0GC4unTSU0ZELae0tAw79kSJYtZWlhg+ZHCT9pedkyu67WjfuFlOidqbmsWNGVk5qKisbPR+evXwRScnB1Fsy469SM/I0ik/fTh/6YpW10Zf785ajxURERHph193b2xY+hsi1y1B317+Wss1Gg1+/3sFugeNxEdf/YiysnI9ZElERERERNQ6zpy7hIFj7sWWyCitZQP79UY37856yKp5qFQqrNm4HSqVWhSfMmE0zDgRHbUzgiBg6+4DqFKKC6UmjR7SpIlaWdxIdIuns/j5n1YMKDWNL3AM9xAX+BWWlGHD7sN1rN327Y07g6T0HFFswpBAONlZQcpiRiIiolZnZ2uD//vwNSREb8T0iaNqXWdfTDyCRs/C3OfeQmqNju9ERERE1DpY3Ej1KiwqxvSHnse7ny/Qar/exdMN8+6fUT2Ad+nqdYNs0X72/CUcP5Uginm6uyFscJCeMiJqWTv2RKG8vEIUGz9qGIyNjZu0v5qdGx0dWNxIHZubsyOk0luDU4Ig4Nr1lEbvRyaTYc49U0SzdqrUavy9Yh3UavUdtmxbKioq8c/KDaKYQi7H7Gnj9JQRERER1WX4kMGI370ef/7wOVydtSf7KSktxfxPv4HfoFFYsmIdNBqNHrIkIiIiIiJqOSs3bMfg8bNx+WqSKC6RSDB6WCjGDg/DpcTrespOd3ujY5GRlS2KBQ8IhHcXwy3YJKrLuYuJuHDlmijWr5c/vDzcmrQ/FjcS3eJRo7hRLQDpJU3Yj5UEvjUuL4g6chYXrqbqkJ1+ZOYUYNNe8WSvjraW6NOdn7FERET65tO1M1b9+S32b1iMoL49tZYLgoC/VmxE98ET8e4XP6KktEwPWRIREXUMCQkJWLhwIb788ks899xzmD9/Pr788kts374dRUVF9e+A2iUWN9Idnbt4BYPGzMKG7Xu0lt1z11jsXvMn7GxtqmNFxSXIzsnTWrctKyoqxqbtkaKYsUKBqRPHQCrlS4Tan8uJ13Aq4bwoFuDXHb7dujZ5nzVf9yxupI5OIZfDzdlJFDtw+GiT9uXt5anVVfV6cioi9x9scn6tbd2WncjLLxDFpowdCgc7G73kQ0RERHcmk8nw0H3TcSFuF957/flaO3ekpGXgwadewaCR07D/oOHOpE5ERERERHSTSqXCK+9/jVmPvYLSGt3q7Wyscf/dkxA8oC8gkSAjKwfFJaX6SVQH15JSEBN3TBRzcrDHiIgQPWVE1HIqKiqxba94LMXczBSjhgQ3eX/FJeILnFncSB2ZqbECTraWolhiftMmQ5/VQwpFjctzlm6OQkWVsvYN2iCNRoMlm/ZDeVtnZAmAORPDIJPx2iMiIqK2Ijy4P2K3LcU/P30GT3cXreXl5RX48Otf4Dt4Iv5YutagJp8nog5MAkj4c+OPsHYmISEB8+fPx7Rp0xAUFARfX1/4+voiKCgIc+fOxYoVK/SdYoMVFRXhyy+/RFBQEKZNm4avvvoKv//+O3bs2IEVK1bg999/x/PPP4+goCCMHDnSoO4bNQ+ePaE6rd28C4PGzMLFGjMZSqVSfPHuy/j3t6/Q2dMN1lYWouWXrhrOTKWCIGDD1l0or9DuYGdrY62nrIhaTlVVFTbvEBcrm5gYY+yIITrts6BQPEuCo719k/dH1F4MCgwQ3T5x+iwKi4qbtK9JY0egk6ODKLZp+x6cPHOuyfm1lqiYOETHimco7dbFA0OC++kpIyIiImooc3MzzH/lWVyMi8Tc2TNE3aRvOnryDIZNno1p9z+Ja0mN71RNRERERETUFuTk5mPcrCfw9U+LtZYNDOyFo5Er4OvTRRS/bGDdG8vLK7Bu8w4Iwq3CE5lMimmTxkIul+sxM6KWERl9WKsIedzwMJiamjRpfzW7NkqlEtjZWDU5P6L2oLOreNLjmFRB9DnTUI5mEkzuLj73mFtQjPWRhjOp2p7DZ3A1JVMUiwjyh4+ns54yIiIiorpIpVLcN30Czh3YiI/ffB6WFuZa66RnZmPei++i/8h7ELk/Vg9ZEhFRRxYTE4Np06Zh2rRpWLFiBaytrfHYY4/hu+++wwcffIBx48bhzJkzmD9/Pnx9fTF//vw23fFwxYoVCAoKwu+//w5ra2u8/PLLWLt2LeLj43HhwgXEx8dj7dq1ePnllxEQEIDk5GTMnz8fI0eORExMjL7Tp1bC4kbSolar8ebH32LGwy9otVa3t7PBjpUL8fLTcyGRSCCRSNCta2fROpevJrdmujo5fOQ4rlwTDzwG+HVH757+esqIqGXtP3hYqxCxh283lJWXo7i4pEkzDeXmFYgGKCQSCezZjY0Iwf16QS43qr6t0WhwsIndGxVyOe6fOUVUUKDRaPDHP6tw4VKizrm2lLhjJ7Fi3RZRTCGX4/4ZEyCVtsNpcoiIiNopV5dO+OP7z3Bs30aMGFJ7N4/1W3chaMRUrQ4gREREREREbd3RkwkYMHomdkdrF1A8Mns69q1fhM4ebvD2chctu3w1qbVSbBZbd+3VmoBvxJBQOHdy1FNGRC0nKSUdR04miGLdungiwNe7yfusWdxob2sDmUzW5P0RtQeDArxEt9NKgMSCpu1riIcEPjWaoUYfPYvoo2ebtsNWdOZSEjbsEX+PcLC1xOSh/fWUERERETWEqakJ3nj+UVyM3YzHHrgbUqn2JfWnzl7E6Hsew8TZT+HKNcO5NpqIiAzXc889h7lz5yIhIQEBAQFYu3YtFi1ahHnz5mHs2LGYOXMmPvjgA8THx+Pll18GcKN4cMSIEUhISKhn761v/vz5mD9/Pjw8PLBo0SJERkZi3rx5CAgIgJXVjYnDrKysEBAQgHnz5lXfXw8PDyQnJ2Pu3LlYuHChnu8FtQYWN5JIbl4BJtz7BD77TvsNoF/vHjiyaxVGDAkWxX26eIpuJ6Wmo7KyqkXzbA6ZWdmI3H9QFLOytMTEMSNq7UZBZOjy8gsQG39cK37s5Bn8/OdSfP3j7/jwyx/w7c9/4vzFKw3eb3Zurui2rY01Z/glAmBmaoKgPj1EsQOHjjSpiBgAunp5anVZValU+OnPpUg4f6nJebaU2Lhj+OvftVqzs949aSQcWABNRERkkPr09MfOtX9h078L4ddN+2LA3Lx8jJx6P9Zs3K6H7IiIiIiIiBrvrxUbEDbpASSlpIvicrkRfvlyPhb+33swMTEGoD0meuVaMjQaTavlqovTCedx+uwFUaxLZw8MHthPTxkRtRyVSoVNu/ZrxXt090ZWTh6KS0qbNFaTnScubnS0t61jTaKOw9fLGY62FqJYVHLjOzcCgFQiwewAKRQ1rmRbsfUAjpy53NQUW9yl6+n4ffUuaDS3TQgN4P6JYTBW8LoJIiIiQ9DJyQG/fDkfJ/euwbgRYbWuszUyGgPHzEJUbNMmticiImqIuXPnYseOHQCAkJAQrF27FgEBAXWuf7MYEACKioowbdq0NtXpcP78+VixYgVCQkIQGRmJkJDaJ1Svqeb6X331FQscOwAWN1K1E6fPIWj0Pdi5T/sN7cGZdyF60xJ09nDVWtbF0100I6FarcbVpJQWzVVXKpUKazdug0qlEsWnTBgNU1MTPWVF1LKSUtIaNMheUFiEleu3NLjAMTsnT3TbgQN5RNWGDAoU3S4oLEL88dNN3t/EMcMRUuNiE6VSiV8WLUP8sVNN3m9zEgQBkfsOYsnK9VqFjRNHhiM0qI+eMiMiIqLmIJFIMGH0MJyM3owFX7yn9f2/oqIS9zz8LL79ZZGeMiQiIiIiIqpfVZUSz7zxMeY+97bWpK2uzk7Yv34xHnvgblG8ZnFjRWUlUtIyWjxXXRUUFGLLzr2imImxMaaMH8UJX6ldOnLyrFaXRQDYsGMvfv5rJb7+5W98+M1v+H3pWuTlFzZ4vzX3yeJGohsFieF9fUSxE5kCiiqbVuDoaCbBdD/xZ5MAYPH6vTh08mJT02wx5xNT8NO/26BUiQumRw7uCR9PZz1lRURERE0V4OeDLct+xvYVv6KXfzet5fkFRRg963EsX79ND9kREVF799xzz1UXJgYEBGDRooZddxMQEIDvvvuu+vbcuXORnKz/bsPbt2/HihUrqjs2NsWiRYuqizu/+uqrNtmZkpoPixtrJWn6j0SfP9Im/yxdswWhE+fgWlKq6JEwMjLCgs/fwZ8/fApTM7NatzU2MUEnJydAIqv+uXw9HZApGvYjlTX9p4n27D+IzOwcUWxwUD909fKsYwttEolEpx9d6Hpsff3olw6v63bC3dUZRkZGDVpXo9Fg1catSLyeXO97T3Zuvui2o4O9nt8LO+iPXuny+jLUn4bxdHOGl4eLKLZm4zYUFhU3eB+3k0gkuG/GZPTrLZ6JRa1WY9Gy1Vi0dDVKSsuatO/mkJdfgB8W/o21m3doLRsRFoRxwxs26wr9RxB0+6HWpdf3cX2/J7bseykZPkEQdPrR67EN9PtZa/zdJpfL8dQjc3DpyB6MHzVUtEwQBLz41sd44c2PGtcJQYfzGjq/D+v6uavTj8YwfyDo8YeIiIiIqOnSM7MxYvoj+OnP5VrLwoP74ciuFQgeoD1Jm5WlBTo52otilxKvt1iezUGj0WDd5u2oqKwUxSeNHQFrays9ZUXUsq7X6MRal5T0TPy1cmODx2xY3EhUu+BeXSA3um1CdAHYe73p529C3aUY11V8nlIQBCzZuA+7D53S+Zxxczl2NhE/L9+OKqV4UvWBvbwxedgAPWVFREREzWH00BAc270Kv339ntZ5gKoqJe578nV8+dPiNvO9hIiIDN+KFSuqOzYCEBUrNsTYsWMxZsyY6ttz585tttya6p133gHQ+PtS0+3b39wntU8Nq3KhdkupVOKV97/C978t0Vrm7OSAVX9+h9BB/WrZ8ga1Wo3tu6ORlpElihcVlzR7rs0lKTkVMXHi1vCdHB0wIiJUTxkRtQ4HezvMvW8GTiacQ1FRCcrKy1FWXoHSsjKUl1dora9Wa7B8zSbcP3MqPNy1u7belJ2bK7rt6GBfx5pEHdOYiMH49Z+11bdLy8qxfO1mPPbgrEYVENwklUoxd/YMGBkZIe7YSdGy+OOncOFyImZNn4S+Pf11zr2hBEHAwcNHsXbTDq2LZABgzNDBmDx6SJPuLxEREbVt1laWWP/PL3jqlXfx+98rRMu+/3UxUlLTseSXr2FqaqKnDImIiIiI2jiJjnPRChodjq3D+Tpd89Zl4iOZosmbxsSfwt2PvID0zGytZc88Ohtfv/8q5HJ5ndu7u7khM+dWgdPla6kYMbTu9UV0ebybeMFkzKF4XE9KEW3fp6cfAvx8GvHc0fFiTZ0u9tRhW50vMtXXsQ01b13pkrf4uezd2Q3nLl5p0KaFRcX4e9VGzJ05GRbmZnWuV1lZhcKiIlHM0c4KEBoxqVNz0+X3pctnh670+frQ5X7rlLehPt4N297cWI7+fh44dOZadWz3dQH9nAV4WDXts2+8twTlKmBfkvj4a3cdwvnEVNw3MRy2VhZN2reuysorsXpnLA6f0u4k2bubO+aMC4ZUUDfx6arLdzMdx2F1+W6nz/cUXej8fVZPdHq89fhZr/O1Agb6HNXX54ek6U0j/tuBjtvr6di6vK51fh/VYXsdmnzo/F4m1eEScp1fW/p6T2rY70pmJMOj99+DmVMn4P6nXsXG7XtFy1/78BtcS0nH95+8CZmsgb9DXZ4nGlX967TU9rr+rvX1t4uuj5lEl/dhA/2eQQZLCnY8Awz7Mfjqq6+q/z9mzBh4eHg0eh+PP/54dYFkcnIyFi5ciHnz5jVbjo2xcOFCFBUVISAgoLrzYlN5eHhgzJgx2LFjBxISEpCQkKDzPqltMuTXMOkoMysHo2Y8UmthY0hQII7sXnPHwsby8gosXb0J8SfOaC3r1rVzs+banA7FHxPdNjIywrRJYxvc0Y7IkLm5OmP8qGGYNX0SHp5zD56Z9wBee/4JzH/1Obzy7GMIrNENrkqpxNJVG5BRyyA/AKhUKuTlF4pijvZ2LZY/kSHq06Mb+vXyE8VOnjmHIydON3mfMpkMD8yaiojQQVrLiopL8Nvif/Hn0lXIKyisZevmlZGVjR9++wvLVm+stbBxytihuGtMBAsbiYiI2jEjIyP8+n8f4aO3XtRatnbzDoya9gBycvP0kBkREREREdENgiDgp0XLMWzqg1qFjSYmxvhrwWf4/pO36ixsFAQBMXHHcexUgiheUlraYjnrKjsnF3ujY0UxG2srjBs5VD8JEbWS/r39cfekUejf2x9+Pl3g6eYMBzsbmJma1DpWkZtfiH/WbK11MtibcvIKRLclEgnsba2bO3UigzUiyBdS6a3Xl0YAlpzRQKVp2oXsEokE030lGOml/Zo9eyUZH/2yCjEnzrd6t6Qzl5Lw0a+rai1s7OffGY9MHQKZjJfiERERtSeWFuZYs+h7PPXwfVrLfl60HNPnPo+ysnI9ZEZERO3Fl19+iaLbJtWaNWtWk/YTEBAgKor87bffdM6tqbZt2wYA6NmzZ7Psb/z48dX/37p1a7Psk9oeVnN1UIePnsSMh19Aanqm1rIn5s7Ctx+9AYWi7llPc/PysWzNFuTmF2gtiwgZiAGBvZoz3Waj0WiQeC1JFBsaGoxOTo56yoiobZBKpTA3N8OksSNQVaVEwvlbJ+MrKiuxZMVaPDznHtjb2Yq2y83L1xowcLAXr0NEwMxJo3AxMQklpWXVsZXrtqC7dxdYW1k2aZ9SqRT3TBkPL083rFyvPeh+5PhpHD1xBr0D/DAkJAi+Pl0hlTbPYJparcapsxcQdfAwLly+Wus6NlaWmD1tLAJ8vZvlmERERNS2SSQSvPniU/B0d8Ujz70BpVJZvSwm7hhCx92DrSv+gHeXtjsZEhERERERtU8VFZV46rWPsHjFBq1lnT1csWbR9+jXu0ed26vVamzeuQ/HT5/T6qjh163tnv+MP3YSavWtrnISiQRTJ46GiYmxHrMiankSiQQBvt61jk9oNBoUFZdi6dqtyM691YU1IzsXy9Zvx/3TJ0Ch0C5yvn1dALCzsfpv8mR9drskajtcHawxNtgfW2POVsfSSoDtiQIm+jRtAlSJRIIp3SUwNdJg02Xxa62iUomlm6Jw/OxV3D0mBE72LVtsXFhchg174motagSA0L4+mDVmULONxRIREVHbIpPJ8MOnb8HLwxWvvv+VaNnG7XsxfNpcbFzyI5wc7fWUIRERGbKVK1eKboeEhDR5X2PGjMHvv/8OACgqKsL27dsxduxYnfJrrKKiIiQk3JgksCkdKGtze6fG5OTkZtkntT08q9IBLVyyChF3PaBV2GhsrMAf332Mn754946FjYnXkrFwyWqtwkYjIyNMnzQGw8KD22x3ppTUdK2uUn16+uspG6K2RyqVYtqkMejm7SWKl5aV4+/la1FYWCSKZ+eIu69YW1nC2JiD4kQ1WVqYYdbkUaJYaVk5fv5zKcor6p4JuD4SiQSD+vfF/FeeRa8evlrLBUHAyTPn8MNvf+ODL37A7qgYZOXkNmkWU41Gg7SMLGzduRfvfPINFv61vM7CxsH9e+HtFx5hYSMREVEHNOeeKdi28k9YWVqI4peuXEPI2Ltx+MgJ/SRGREREREQdUlJKOsInP1hrYePIIYNxZNfqOxY2lpWV4++VG24UNtbQu4cfxgwPb9Z8m9PlxGui28ED+qKzh5t+kiFqI6RSKWysLfHA3RNha2MlWpaclonlG3ZApVJpbZedJy5udORkr0RaxgT7w83RRhTbeVVAUpFuRcBjukrxRKAUVrVchnD2SjLe/2kFFizbilMXrkGj0eh0rNsJgoCL19Lw++pIvP390loLG02M5Zg9Phj3jg1mYSMREVE7J5FI8PLTD+Pf377SmhAl7thphIyfjYtXruknOSIiMlgxMTGiro23F/E1Ra9e4gZlK1as0Gl/TXF78eHp06ebZZ+3F0mmpKQ0yz6p7WHnxg7mm1/+wkvzP9eKe7i5YM3i7zGg751bvx49mYAtu/ZBoxGffLQwN8e90yfAzcW5WfNtbjUH8ZydHGFZ44JLoo5OJpPhnikT8M+qDbienFodLywqxt8r1mLufXfDwsIcAJCdKy5udLS3a9VciQxJv15+6HfmAo6dPl8dS0pJw89/LsUzj95/x4kF6mNtZYkn5t6HuGMna+3iCABZOblYs3E71mzcDlMTE3i4ucDT3RWe7q7o5OQAhUIOuZERBAFQqVSorKpCWkYWklLSkJyShuTUdFTd1oGpNjZWlrhv6lj09GNRIxERUUc2fMhgHNi2EuPveRgpaRnV8eycPAyfMgfLfvsGd40fdYc9EBERERER6W7PgcOY9diryKlRlAQArz77KD5+83nIZLJatrwhKzsX/67bivyCQq1lI4aEICx4QJud8DUvvwB5NSaq7R3gp59kiNogSwtzPDBjIv5cvgHFJSXV8cSkVKzeshv3TBolKlTKzi0Qbe9ox+JGopqMZDLcPy4IX/wTWX1NkUYAfjuuwf8GSmFv2vTPzJ6OErwdIsXq8wLi0rWLJc9dScG5KymwtTJHWD9/BPp3haO9NaSN/JwWBAF5hSU4fek6oo+cRUZOQZ3r+ndxxezxwbC1Mm/s3SEiIiIDNnPKOLh0csSUB55FwW2NIhKvJyNk/H3Y8PcChA7qp8cMiYjIkCxfvlx0u2fPO9fy1KdmceTN4kkrK6s6tmh+txc3xsbGNss+by8Atba2bpZ9UtvD4sYOJDevAG9/+p1WfFjYICxf+H9wdLhzUVJGZjY279yn1e3JpZMj7p0+WasrQ1tUs7jRu0tn/SRC1MbJ5XLcN2My/vp3DdIysqrjuXkFWLJiHR66bwZMTU2QnZMr2q6+9xGijm7mpFG4lpyGvIJbX7QvJ17HgoVL8OTDs2FqatLkfd/s4ujXzRvbIvfj8NETqKysqnXd8ooKXLxyFRev1N55sbHMzUwRGtQHoyOCYabDfSAiIqL2o6d/d8TuWI2J987DyTO3OpyUl1dg2gNP4fvP5uPpR+/XY4ZERERERNSe7Y85gjEzn4BarRbFzc1MsfiHTzF90ug7bp94LRkrNmzTOscqN5Jj2sTR8Pf1afacm1PNMVELczM4d3LUTzJEbZStjRUeuHsiFi1fj7LbJo08f/kaNuzYjyljh1YXMGfnsnMjUUN4dLLF2GB/bI05Wx0rqAR+OHKjwNHauOkFjmZyCR7oJUGgs4B/z2pQVKm9Tn5RKTbtO4JN+47ARCGHu7MDPF0c4OniCBdHWxgr5JAbySCRSKBUqVClVCEztwBJ6TlISs9GcnoOSstr2fFtTIzlmD6iPwb39mmzkxwQERFRyxoyeAAObvkH4+99AteT06rjefmFGDnjEfzz0+f1nncgIiICtIv/bu9Q2BS1bb9t2zbMnDlTp/02xu2FlEVFRVixYoXOx7+9YFLXx4jaLhY3diC//r1Cq5PTi08+hM/eeRFGcnkdW92SnpmtVdjo360rpk4YCYWxabPm2hJKS8uQlpEpivl09dJPMkQGwNjYGHPumYpFS1eJOjRmZudg2eoNuH/mVGTn1Ojc6GDf2mkSGRRLCzM89/BM/N9vy1BUUlodv3z1Or79ZRGemHsfbG10m1XE2soSs6ZNxJTxoxB37CT2H4xDemZW/Rs2QRdPNwwJDkS/nn6Qy/m1koiIiMTcXJ2xf9My3PPws9i590B1XBAEPPva+7iekobP5r8i6oRARERERESkK0EQ8OK7X2gVNnbr2hnr/voBPeopTKyqqsLKjdu1ChutLC1w74wpcHF2avacm9uVq9dFt727eLIAg6gWjva2mDNtPP5atRmVVbde8yfPXoRCLsf4EaFQqdQoKCrW2o6Iajcm2B/nr2UiMe3WRMk55cC38Ro801+3Do4A0MtRAu8QKXYkCohNFVCmqn29iiolLiel43JSuk7Hu0luJENQQBeMD+vNbo1EREQE/+7eiN26DBNnP4Vjp25N7FBZWYV7Hn0RX73/Cv73xIN6zJCIOhKJ5MZPR2doj0FycrKoIyHQPIV7VlZWov0mJCTovM/GqNlZcf78+QgJCdHpvm3durX6/2PHjm3yfqht49VjHURVVRV+/GOZKHbvtAn46v1XYWTUsGIEhUJcAGltZYF7poyDQqFotjxbUs1BPIVcDk93Vz1lQ2QYzMxM8cCsaVrFVsmp6Vi2eiOycmsUN9qzcyNRfZwc7PDM3Hu0Ohwmp6bjo69+RGzcMa3JBJrCxMQYQ0IG4u2Xn8b/nnwYQYG9m6XLsq21FUKD+uCNZx/CK0/ej0GBPVnYSERERHWysrLEpn8X4qH7Zmgt++qHhZj92P9QUXHn2dCJiIiIiIgaIyr2KI6fPi+KTRodgbjty+otbASAgsJirb9T3Fw6Yd79dxtEYaNKpcLV68miGCd8Jaqbq7Mj7ps6FvIa103En0zAngPxyMkrEI3bSCQSONjZtHKWRIbDSCbDk9PD4eogvsYguwz4Jk6DlGLdx0HN5BJM9ZXiowgp5gRI4GlV/zZN5WRnhRkjB+CTZ6Zj9vjBLGwkIiKias6dHLFv/WKMHzlEFBcEAS/N/wIvvP2p1sRLREREN8XExGjFbu962FQ1iwvPnDmj8z4bIyAgQCs2bdq0JhdZFhUVYeXKlQBuPD4hISE65UdtF69E7yBWrN+O9MxsUayxs4KYmoiLMJRKlUHN8Hnl6jXR7S5eHpDJZPpJhkgPBEFo0mvW0tICD8yahj//WYni2zrNJZy/iKvXkhHg3716v+zcSNQw7i5OeOHRe7Fg0UpRB8fyigosWbkex04lYPbdd8HGWvc/VCQSCbp5e6GbtxcEQUBhUTGuJ6ciKSUNSSlpSE5NR3FJ6R0LKs1MTTBt/HD08vOGpQUH7IiIiKhx5HI5/vj+U3h5uuG9z74TLVuxbgvSMrKwbsnPsLPVrYM1ERERERERAHzz69+i295eHli76NsGjwsaG2tP7Hrv1PGwMJBzo8kpaai6rQOdRCJB1866z/hN1J51dnfBzMmj8e/6HVBrbl18HB13HCnpmaJ1bawsOekjUT3MTBR45u4h+ObfvcguKKmOF1QCXx7SYGxXCUZ3kUAm1e2aI4VMgmA3CYLdgOuFAg6kCEjIFlBUVf+29eXv6+WMsMDu8O3sbFDXRhEREVHrsrAwx/q/f8DTr32EhUtWiZZ9/9s/SEnNwJKfPodpjUnwiYiIaiv2a47OjR4eHkhOvjX5XWt3bgSAMWPGYMeOHdW3i4qKMG3aNDz66KN45ZVXGrWvX3/9tboT5YcfftiseVLbwjOuHYAgCPjml8Wi2JDBAzCgb89G7admh6nyisomF0u1NkEQcDlR3LnRp4uXfpIhamVXrydj47ZI5BcUQi6Xw1ihgLGxAgrFbf+Xy2FsrICxQgGF4sa/xibGovjYkRFYvWEbqqqUkMmkKCsrR05ePs5fugK/bt6wsrSAiYmxvu8ukcFwd3HCi4/Pxg9/rkBufqFoWcL5S/jwywWYMmEUggf0hVwur2MvjSORSGBjbQUbayv06ekPAKiorETUwThs3rEHqlpmC3N1dsSzc2fC2kr3ro9ERETUcUkkEsx/5Vl4urnisf+9BZVKVb0sOjYeoWPvwdaVv6MLL7glIiIiIiIdXLxyDZt27hfFnp83p1ETnprWMtZRpVTqnFtruVxjwldXl04wNzfTTzJErUytVkOt1kAuN2r0dQw+XTwwfcJwrNocKZoQcs+BeCiMFXBzdgQAONrbNmvORO2VtYUp/nfvMCxYFYW0nFtjoWoB2HJFwKksAXN6SuFm2TzXHHW2lqCz9Y19FVQISC4CkooEJBcJSCoGiirvvL2RTIpxIT0xoGdX2FtbGMS1UERERNQ2GBkZ4Zev3oWXpxve+vhb0bK1WyKRPv0RbFiyAA62LdhumoiIDM7tBYg31ey62BSWlpa1Hqs5Cicb6vHHHxcVN970+++/Y8eOHfjuu+9q7fBYU0JCAn7//XcANwomx44d2+y5UtvB4sYOYN/BOJw4c14Ua2zXRgBaM4cIgoCKikqDmFEkIzMLpWVlophPVy/9JEPUilLTMrBs9UYo/xt0VyqVUCqVKCktrWdLALWcrC8uLsGJM+egVqtRWFSMwqJilJSUQiGXY9SwsOZOn6jdc7K3xetPP4hVW3Yj7rh4dpTyigr8u2YTNm7bjZCB/RA+eAAc7O2a7djpGVmIio3H4SMnUFFZ+2je0MH9cdfYCBgrtGcqJyIiImqKh+6bDlcXJ9z90DOizvAXLiciZOw92LTsNwwI7KXHDImIiIiIyJB9t3CpqCjJxtoSD826q1H7kMvlMDIyEk3KUl5RT0VEG8IJX6kjEgQBu6PjEHv0FNRqNSQSSfVErwq50a1JXuVyGBvLxf+XG1VP+GpirMCgwJ7YcyAeMpkUMpkMpWXluJ6aASOZFJ0c7eFob6Pvu0tkMKwtTPHCrKH4ec0BXE3PFS1LLga+OKTB6C4SDOssgZm8+YoJbUwksDEBejnd2KdKI+BougZrLwClKu31rcxN8Mw9w+DmZAtIGz4hAhEREdFNEokEbzw/Dx6uznjkhbehVN760hF75ARCxt+Hrct+gk8XTz1mSUREbUltxY0teazWLG4MCAjQ6t54ey4N6eKYkJCAadOmVe/v+++/b7F8qW1gcWOz0+Vkm1D/Knc8tLTW8Le//i267e3liYljhovXFzT17t7MxBgQxDmWl5ffmL1UemNfgiAgL78AGVnZSM/IRlFxCexsrdHVyxNuLp1qnxW1jryb0+XEa6Lb9na2sLW1ATS1nLVsJR1zlreOeJ9Ra5Fga8jLL8DS1RugVKmaLQdLSwv06uGLUwnnoVKpoNFoYCQ3QidHBzja2zfLMfSvgz5PSW/MzUzx0N0T0a+nH5at346iYnHxcWlZGXbtO4DI/QfRw9cHIQP7oUtnD9hYN242L0EQkF9QiCtXk3Dg8BFcunKtznUd7GwwZ/p4dO9a44SaoMN3lQ75uacjPmZERDrT+e8uXT77qE6jh4UjastyTJj5KNIyMqvjmVk5GDp5Nlb88T0mjB7WuJ3qfG5Bl9+1js8TXZ5nOj1Hdc1bh235PYeIiIiIWkBefiH+WrFBFJs3ZwYsGtm1UCKRwNTEGMUlt8YRy8rLmyXHllZUVIzMrGxRzLtLZz1lQ9R69h6Mx4G449W3BUFAZWUVKiurGrC19h+4eQWFuHwtBQCQmpEFtVoNQRBgbKyAox07NxI1hrmpMZ6fNRRbYxKwK+6CaBICtQBsSxSw+5qAAS4SDPGQwN2q+c4b5VcIOJgiICZFQFEdbwd9urlj5uggWFuYNttxiYiIqOOac/ckuLk4YdpDz6OwqLg6fvlqEkIm3I9N//yAQf166zFDImqPpP/9dHSG9hi0ZnFjUVFRqx3rpo8++gixsbF1HvtOXRwTEhLw0EMPAbjRsZGFjR0DixvbuYtXrmLTjr2i2POPP1B7kWE9apulNCklHUkp6UjPKUB6ZhYys3JQWaV9RnDfwcMwVijg5emOrp090NXLAw72dq1W4FezuJFdG6m9KyoqxpIV61BWXtHs+7axtkJP/+6Ijo2HuZkZBgf1g6ODPVydnZr9WEQdSW9/H3h3fqTWLo7AjYH4hPOXkHD+EgDA2soSHm4u8HR3hae7KyzMzW58VstkUKnVUCqVKCwuQVJKGpJT0pCUkoaS0jKt/dbEbo1ERETUGvr09EfszlWYMPNRnDl3sTpeVlaOu2Y/jp+++gCPPThLjxkSEREREdEd6TzG1/yTyfz2z2rRuIiRkRGefeTeGoetf8JXADAzNUZxSUn17fKyckDQVI9tajQa5OTmISMrB+kZWcgrKICFuTm6eLqjS2cPmNdaUKnDY9bAvK9cvYrbH1sTY2O4u3aC0MDtaz+2rhOj6HBs3Q6sp+PqyJAnWtLT5D1HTpxB1KGjTT92LdxdO0GlVuNacho0GgEajQBHextYWZj/17nxv3x1us/6em2gGZ5nerrfOuetz2PrQKdj6/g8a6b7LZcBd4UHoI+PC5ZsO4KMvGLR8ioNEJMqICZVQBdrINRdAh9bCexNGz+BXXGVgGsFwKE0DU5nA5o67oK5iQL3jOyH/n4eN45RPTm6Lo+ZHi+l1fW7mU6Tx+lwbJ3zbvnvV22PAX9XoFak6/NEl9e1oZUVtAW6vBfqczJJPb6H6zYDpw7bNmz7YeGDEb35H0y49wkkp6ZXx3Py8jF8+qNY9suXuGvc8EYeVtfHW4fu1Lp+Zgo6NKHRqHU4rq5567C9Pt8LOcksUYdnY2OjFWvNQsqbrKyssHbtWkybNq3OAsfaujh++eWX+P333wEAL7/8MubNm9dqOZN+sbixnavZtdHG2goPzZra5P2ZmZqgqPjWQN76bbtv/Eda/xffyqoqXLiciAuXEwEAVpYW6NrZE97e3ujq5QkLC/Mm53UnFRUVoj8QAM5QSu2LIAgoLCpGUnIqkv4rYMrKydVaz9vLE8PCB6OyqgpVVUpUVlWhsrLytv9XoUqpvDGLaVUVqv6L3Vy/SqmsnkXRztYGfXr6IfF6CtRqNawsLRDg37217zpRu3Ozi+PAvgGIjD6M85ev17luYVExCouKRcUATSWRSNDLzxujIoLh3dld5/0RERERNYSHmyuit67AjAefxu6omOq4RqPBEy++jWtJKfjorRchlXIgnIiIiIiI7qyqSokFf/writ0zeTTcXZ2btD9TExPR7bTMbChVKmTkFiEjMxuZ2TlQKrUvzjt64gwAwMXZEV07e6Krlwc83V0hl8ublEdjXU4Un1Pu6uUJqVQKQWOoF+8T1U2j0SDhwhVs2X2gRfbf2d0FKrUagARO9rb4/O1nkZtfCEd7dm4kaiovFzu8/sAIbI05h13xF2qtnbxaCFwtFAAIMJMDHpaAp7UEnpYSWJsAcilgJL3R9VGlBkqVQHKxgKQiAclFQH4D5n/u4+OGmaP6sVsjERERtZie/t0Ru305Jt77BE6cOVcdLy+vwLSHnsN3n7yBZx6ZrccMiYhIn+oq9rOysmqR4xUUFLTIfuvj4eGB3bt34/nnn0dMTEyd693s4gjcKHgMCAjAd999Bw8Pj9ZKldoAFje2Y7l5+fhr+TpR7P/Zu+/wKKr1D+Df2ZLeSUgPvYYSAqEECL2LIB2xIIpdsZdrueq9v+u1XBW7iFKlSVF6L4GE0Hvv6b333ez+/kAXJptAyCQ5u8n38zzz6HmnnHeW3c3uzrznPPnIZEVFhPZ28uJGJXLz8nH89FmcOHuzKKNj+7YYNWwg7MpdLFTq2o04GG67YKdWq9A0iIUbZL0MBgNS0zIQG3+rmPFur0tf78aY9MAo2NraVrtfo9GIUllhZCmycnJgZ2uH5k0DecMxUQ1q36oZ2rdqhpS0DEQeOIaYo6dRVFxS4/04OTqgd1hn9OkegkburjV+fCIiIqK7cXVxxoblc/HkS+9g4XL5bxj//epHxMYn4pevP1b0XYaIiIiIiOq/FWu3IDE5VRZ76cmHq308ezv5d5CYIycAAJKqakWKSclpSEpOQ9SBI9Bo1Aj090WL5s3RvGkQfLwb18o1FYPBgKvXY2Wxls2b1ng/RKKUluqQkJyK2IQkxCYkIy4xBaWlOpSftWV4/3A0C/JDSanu1uCud/r/0hKUlupvXv8s1aG0VAedXg9JktCiSQBcnBwR5O+DZkH+aBbkL+bkieoRrUaNMREd0KW1P7YevIATlxJhqGSGyEIdcCETuJB5s9hRqeb+nhjUrTU6t/K/5xkhiYiIiO6Vn09j7Fm3CJNmvIQtu24NymI0GvHi2//BjbgkfPI+B3olIqLal5eXJ6xvFxcXzJs3Dz///DM+//zzSrf7e3bJ4OBgrF69uq7SIwvC4sZ67KcFy1FUdGtIMo1Gg+efUDbSh4uzI1LS0u+6naODA3y9veDq4oz4xOQq7XPq7HkUFRfjwYlja/TD+uWr12XtJoEBsLGxqbHjE9U2nU6HhKQU08yMcQlJKCktrfL+7m6umDZxjOKbgSVJgq2tLWxtbeH8V8y/miMeE1HVeHs1wsT7BuP+oRE4fOIcDp04ixvxyff0HlCevZ0tmgX6oUdoB4R0aAOthh8HiYiISCwbGxvM++5TBAb44f/+951s3ZKVa5GYnILVC3+Am2vtjFBHRERERETWzWg04sufFspifXuGoltIcLWP6WBfczM56fVluHYjHtdiEwEAvj6NMf7+kfBs5FFjfQBAQmIyiorl01W1bNakRvsgqksFhUWmQsbYhGQkpaTBYLhzcVO/nl3Rs2vHe+zJ/JgGg+GvAsibRY96fdk9HpOI7ibIxx1P3N8T2flFiDp5DVEnryEnvwrTLt4jG60G3ds3QURIC/g3dqvx4xMRERHdibOTI9b+9j2efu0DzFsiL9T43/fzEJeQhPnf/Ad2dhzolYiotqSlpdXKcR0dHeHk5FStfXNycmo4G8s3c+ZMhIeHY9asWaZCxoqcOXMG48aN48yNDRDvZq+nSktL8d0vv8lik8YMR4DCQqSunYNx+VosjLeNmubm6gJfX1/4NPaEr3dj+Hp7wcnJUTbKWX5+Aa7FxuPq9VhcuR5b6Sxzl69ex7adkRg2uL+iPP9mNBrNihs5QilZusLCor9mZLxZzJiUkoqyMsPddyxHq9GgdctmGDqgr6IZW4lIPFsbG/QO64zeYZ1hMAJpGZm4EX/zYn5sYjJS0jJQWqqHTq+DwWCESqWCVqOBra0Wvo09EeTvY1o8Pdw4EikRERFZHEmS8K9/vIygAF88+9o/UVZ266bB3fsOoM+ISdi44lcEBfgJzJKIiIiIiCxR5P4jOHbqvCz28lOPKDqmq0vVbkpxcXaCr48XPD08kJqegRtxCX/NJFe5pORU/LZiDWY++iAcHGquiPLyteuydmNPT7i4OFe8MZGFMRqNyMrOxY34JFNBY0bWvd3kFdqxLfqHd62RfFQqFezt7VCDdc5EVAk3J3uMCm+P4T3a4tSVJOw/fR3XkjJRUFT9wV5ttGoENnZHaNtA9AhuCnvbqs28TERERFQbtFot5n71bzQJ8MUHn8oHel3x52YkJqfij4XfwMPdTUyCRET13Pjx42vluM8//zxeeOGFWjm2Es7OlvubcGBgINq3b3/H4kbgZoHj4MGD8dprr2HmzJl1lB2JxuLGemr5H5uQlCKvMn/p6UcVH7dtq+Z48pFJSElNh6uLM3wae8Le3g5Q33kmRCcnR3Rs3wYd27eB0WhERmYWrl6Pw9Ubcbh6IwE6vd607f5DR+Hl2QihIfc6oqKcXq/Htl17kZMrn0aXxY1kiWLjEnD81FnExiciPTOrWsdwsLdDUIA/ggL8EBTgB1+fxlCr1TWcKRGJplJJ8PZqBG+vRujexXzk8ZvFjSxeJCIiIus085EpCPDzxaQZL6CgoNAUP3vhMnoNnYANy+cipGN7gRkSEREREZGlKT9rY4umgRg9tJ+iY3Zs1woHjp5EQWGRKdbI3Q1+fv7w8faCr7cXfBp7wdHRQbZfWVkZ4hOTbw74ei0WCUkpskFj/5aVnYPlq9fhkanja+xazuWrN2Ttls05ayNZLoPBgOTU9L9mZUxCbHwS8m/7HeBeNHJ3RbdO7dEjtAMHdySyYmq1CiGt/RHS2h9GAJm5hYhLyUZsShZik7OQmJ6DklI9SvVlMBiMkCRAq1HDRqNGY3dnBPm4I8jbHYE+bvDxcIVKpRJ9SkREREQmkiTh/deeRZMAP8x85Z/Q33bP9L4DR9F71EPYuPRHNGsSIDBLIrJWKunm0tBZ02OQm5tbp/1lZ2fXaX8V+eyzzzB37lwAwLBhwzBy5Ei89957d3wsPv/8c2zatAnz58+Hi4tLXaVKgrC4sR4yGo348of5slhEr27oprBY8G++f12wqy5JkuDZyAOejTzQvWtnxCWlY8HS32Uz023YugONPNzRJKh6H9TTMzKx8o8NSE6VF3i6ODvBy7NRtXMnqmm5uXnYumsvTp+7eM/7eri7yooZG3m484IdEbGwkYiIiKzeiMH9sGfdEtw3dSaSbxu4KSklFRGjpuL3+d9g2MAIgRnWjNJSHVIzslFaWopSnR5lZQZoNGpoNWrY2mjh1cgdNlr+dEdEREREdCcXr1zHuq17ZLFZMx9SXDDo7uaKZ6ZPQUJSCuzt7ODt1Qi2tjaQtHeexk2tVqNJoD+aBPpjQN9eKC4uwfXYeFy9EYvzl24gN+/WoKw34uKxYcsOjB4xRNH1nbKyMuzYE4WEpGRZnAO+kqXR6/U4euoczl+6jvikFJTqbpvltIIi4IqoVBJ8vb0Q5O+DIH8fBPr5wMnBtpYyJiJRJElCI1dHNHJ1REhrf7P1fxc3Vv73k4WNREREZJkenTIWfj6NMWHGS8jLLzDFL1y+hvCRD2Ldb9+jW0gHgRkSEVFdqM1Cvby8PLOYm5tbrfV3N2fOnMGsWbMQFxeHwMBAfPTRRwgPDwcAhIeH491338WWLVvuuP+gQYMwf/58BAebTwhD9QfvkKqHdkcdwPHT52Sxl595TFA2dxcU6I/Rw4fgjw233pTKygxYvnodZj46Fe73MNW60WjE8ZNnsHHrTtlskH8bGNGbxV9kEcrKyrD/4FFERh+UX7irhCT9daHur2LGQH9fODs71UGmREREREREdS+0cwfs37ISIyfNwLmLV0zx/IIC3DdlJn784l94/KFJAjO8N6WlOsQnpyE2MRWxiSmITUhFclpmhTO4/E2lkuDr1QiBfo3RxL8xAn294e/jyYJHIiIiIqLbzP75N9nnajdXZ0yfMqZGju3k6IA2LZspOoadnS3atm6Btq1bIKJ3KebMXyIrcDx64jQae3miZ1hotY6fkZmFVWs3ITE5RRbXarQICvBTlDtRTbp8LRabdu5DRlbOPe1nY6NFgG9jNAnwRZC/L/x9GsPGRivfyFhWg5kSkTXgYK9ERERkzYb0D8fedYsw6sFnkJB06/t8SloG+o+djmVzPsd9Q/uLS5CIiKiGbN68GbNmzQJwc7bGr7/+WrbexcUFX3/9NaKjozFr1qxKZ3HMzc3FuHHjsHr1ahY41mO8G6oe+urHBbJ2i6ZBFv9BN6RTMNLSMxB14LApVlhUhKUr/8Tjj0yBre3dR1ssLi7G+s07cPrcBbN1Go0awwb2Q+eO7Ws0b6LquHLtBjZu24WMzOxKt9FqNAjw90VQgB+aBPrD39e7Sq8DIiIiIiKi+qJJoD/2bVqBcY88gz1RB03xsrIyzJz1D9yIS8SHb82y2EGMjEYjLl9PQOTBEzhx7grKygz3tL/BYERCSjoSUtIRc+wsgJu/b3QJboWIsE5oFuhjsedORERERFQXMrNysGD5n7LYzIcmwMnRQVBGd+bk5IipE+7Hr4tXQHfbwJdbduxBIw93tGpxb4WUJ06dxYatu1CqKzVbNzCiFzQa3gpA4mXn5GLzriicv3y9Sts7OTqYZmUM8veBT2NPqFScgY2IiIiIiOqXTsFtsH/TEoya+gxOnbtoihcWFmHsIy/gu0/ew1OPWs9Ar0REROUtX74c77//PoCKCxtvFx4ejh07duDzzz/H8uXLK91u+vTp2LFjR63OfEni8IpGPXPxyjWs27JLFpv11CNQq9WCMqq6Qf37IC0jAxcvXzPFUtMzsGrtRkwZP+aOFy3iE5Kw8s8NyM4xr9Zu7NkIE8aOQmMvz1rJm6iqsnNysWVHJM5dvFzhens7O4R3D0XzpkHw8fayitctERERERFRbXJ3c8Xm3+fhseffxLLV62Xr/v35t4iLT8RPX/7bfNYGgYqKS3DwxHlEHjiB5LTMGj22Xl+GQyfO49CJ8wjw8UJE907o1qkNbC3o/ImIiIiI6sqcxStRWFRsams0Grzw+FSBGd2dr483HrhvGFasufX9xmg0YuWfG/DEI1Ph5dnorscoKSnBhq27cPLMObN1Go0GQwf0RVho5xrNm+he6XQ6RB86gb0HjkJfVvnMip4ebgjy90WQnzeC/H3g7ubCgXyIiIiIiKhBCPDzQeS6hZjw2EvYsTfGFDcYDHjm9Q9xPS4B//ePWRzwhYhIoVWrVsHLy6vGj+vo6FjtfV1dXWswk7tzdnau0/42b95sKmwMDw+/Y2Hj31xcXPDRRx9h+PDhlc7imJubi+nTp2P16tU1njOJx+LGeua3letkbTdXF0yf8oCgbO6NSqXC+PtH4peFy5CanmGKX7x8DTt278OQgRFm+xiNRuzbfxC79u6HwWA+A0K3Lp0wbFA/aLW8yY/E0ev1iDpwGHv3H4Jeb37xTpIkhHYKxqB+veHgYC8gQyIiIiIiIstla2uLxT99gaAAP3z69RzZugXLViMhKQUr538DF5e6/TG2vJJSHTbsjMHeQydRWqq7+w64+X3Q1sYGWq0GarUaen0Z9Ho9iktK7rpvfHIalqzdgdVb9qJ/zxAMjwiDVsuf+oiIiIioYSgt1eHbX5bKYpPuH4oAPx9BGVVd+7atMaBvOHbtjTbFSkpKsXTln3jikal3vFaUmJSClWs3IjMr22ydp4cHJowZAR/vxrWRNlGVGI1GXLxyA5t3RSGrgoGJASDA1xu9w0IQFOADR4e/Zlo1ml/rJyIiIiIiqu9cXZyxYekPePKVf2LhirWydZ98PRdx8Un4Zfa/YWvDa4BERNXl5eUFb29v0WlUSW5ubq3MSujm5lbjx6xMbm4uZs2aZWrPnj37nvYPDw/HoUOH8P7771c4i+OZM2ewefNmDB8+XHGuZFn4aaciSkYCNBqVdKxg35vKyuQ/+pfqdCgsKoaTU/Urw6tCkpSMDHLrvG1t7TB1wlj8vGAJCouKTPGoA4fh5emJkE7BplheXj7WrN+Eq9djzY5oZ2uLMaOGol2bVlXu+97TVjirHi/Q1C1BI3xeuHQFm3dEIis7p8L1/r7eGDlkAPyt4GL7veGIqlRFHH23bin6nCK4bz5XiKieUDLyvFHk+7giSvMW95hxpoB7V1uPmVqtxicfvImmQQF4/o0PZAMcbd8ThR5DJ2DWU9MxZdxouLtVY4Q7qfrPFaPRiEvX4rF4zRakZ2ZX3oUkwaexF4IC/BAU6IegAD8E+PrA1tbGbNvi4hLEJyYhNj4RsfFJuBGfgNS0jAqf08Ulpdi85yCOn72MR8YNQRP/qn6/FPfaVP6biMDXpoLnSoWfia32vZ2IiIhIrL0HjiAxOVUWe/GJaYKyuXcRvXsgLSMDp89eMMUys7KxYs16PDxlHNRq+XVIo9GI/QePYvuefRUO+BraqQOGD+4HGxvz7xdEdSUzKwebdu7DpWvm1+8BwNHBHoP79kBIh7b8zYWIiIiIiOgvNjY2mPfNfxAU4Id/f/GjbN2S1RuQmJKK1fO+gptrzRe7EFH9IIF3bQPW9RjURgHj37Kzs+u0v/JuL2z86KOPqt33Rx99hPDwcNnx/jZnzhwWN9ZDLG6sZx6dPBaffP0zyspuzg5XWFiET7+di88/fFNwZlXn7u6GyePux8JlK03nAQDrNm+Dh7sbggL9cfHyVfyxfrOsAPJvQQF+GH//SLjygzwJlJmVjc3b9+DilWsVrnewt8Pg/n3QpVMwL94RERERERFV0TMzpiHQ3xeTH38RhYW3fhO4cOkqnn3tfbz8zr8xZsQQTJ86HkMG9IFGU7s/fZWUluLPrXuxJ+ZYpTVqjT0bISI8DD27dbnjDCy3s7OzRcvmTdGyeVNTLC+/APsPHcXe6EPIqGCWluS0THw2ZwWG9OmKkQN6QFvL505ERERENUTkYJiKBi9V3Hn1dzUaYavVmoWvxcaje5cOd+lW4CAftx9FkjBm5FBkZmUjMSnFFL8eG4dN23Zh1LBBputH+fkFWLNuI65UMuDr6OGDENyu9V+R+jZ4Rg0PLlJnlPStMG8B511aqsO+g0cRdeAYygxlZuslSUL3kA4Y0DsMdna2f0XL52mlgzMqeQ9X/G8lKG+lFJ23wrwV/VubP7frpF/RRP17KX7IlHzGEbWvQtZ634na9u7b3Imi56iCfUU+3tb8nqKEsL+5Sj+bKclb4N9rlYJJH5ROGCGqLEHxgN01k8a99ytyMEgrfS8U+VvIHfqWAHz0j5cRFBiAZ177p+ze6d1RhxA6eBKemj4FD00cA3/fasw+plLwnmLQV39fAFDwUVrZoKMK30eVPFeETn4j6DnOQV6J7pmLiwtyc3PrpC9X12oMEl4NZ86cQXR0NICb5zd58mRFxxs+fDhWr16N6dOnyx6rM2fO1NoslySOyCtWVAtatWiKhyfeL4t9/+sSJJUbudTSNQkKwKihg2SxsrIyLF+9Fus3b8eS39eYFTZKkoT+fXph+rRJLGwkYXQ6HXZGRuO7uQsrLGyUJAndQzvjhSenI7RzBxY2EhERERER3aP7hg3E7rVL0Nirkdm6kpJSrPhjA0ZOnoGgTn3w5gf/xdnzl2oljys3EvCfbxZi937zwkZJktC5Qzu8+OSjeP+NFzAwIrzKhY2VcXZyxNABffHh2y/h2ccfQod2rc2+UxqNRmzdexj//WEpYhOt67cgIiIiIqJ7ER4Wgo7tWsliH37+o+zmP0un1WoxdfwYODs5yeKHj53EwSPHAQCXr17HD78uqrCwMdDfF0/PmHZbYSNR3TIajTh74Qq+m7cMkfuPVFjY2CTAF08/MhEjBvW5rbCRiIiIiIiIKvLEwxOxbsmPcHJ0kMWvxybg7Y/+hyad+2PEpMexbPUGFBUVC8qSiIhqQmBgoFksJydH8XHz8vKq1FdtWL58uen/J02aVCPHDA4Oxvz5883imzZtqpHjk+VgcWM99O6rz0KtvjUCTnFxCT75Zq7AjKonNKQjeoV1lcUKCgtx+NgJs21dnJ3w6NQJ6N+3F1QqPq2p7hmNRpw9fwnf/rwQkdEHUVZmPvJKUIAfnpr+IEYOHQB7ezsBWRIREREREdUPYaGdELN1FToFt610m6TkVHz69RwEhw9D98Fj8f0vi5BZwYyH1XH8zCXM/mUF0jLNj9esSSDef/0FPDV9Ktq2blHjv1OoVCp0aNcazz7+EN5++RkE+vuabZOclokvfvkdZy/dqNG+iYiIiIgshUqlwgevPSOLnb98Dcv+2Cwoo+pxdnbC1An3Q1tuJsrN23dj1Z8bsXj5ahQUFMrWSZKEiPDumP7gBLhxwFcSJC09E4t+X4cVa7cgJ9f8hiknRweMGzkI0yePgXcFgxMRERERERFRxYYPisCedb/Bp7GX2TqDwYAtO/fhwSdfgV9wHzz96vuIOXwcRs6QR0RkdQICAsxiNTGTY0UFksHBwYqPWxV/z9oIAL17966x4wYHB5vNAllXs15S3WEVWD3UvGkgpk8dJ4v9tGAZEpJSBGVUfUMGRqBVi2Z33KZtqxZ4esbDaNqkbirKicpLz8jE4uVrsOKPDZVfvLtvGB6bNhE+3uZfOImIiIiIiOjeNWsSiCO7/sT6ZXMx4f4RsLGxqXTbQ0dP4rnX/wnfdj0x6bHnsXHbLuj1+mr1G3P0NH5euhb6cjPCaDUajLtvGF597nF4N/as1rHvVYCfD9548UmMHj5QNtAVAOh0evy4ZC2OnqmdmSuJiIiIiEQbO2IgunSQD3jy0Rc/Vvuzvih+vj4YO2qoLGY0GnHq7HmzbZ2dHPHIlHEYGBFu9h2AqC6UlJRi6+5o/LBgBa7eiDdbr1KpEN6tM16YMRWd2reGJEkCsiQiIiIiIrJuXTq1x/4ty9E9tFOl2+Tk5mHOguUIHz4Z7XuNwH9nz7HK+8SJiBqqjh07msVqYubG8kV/dTVrY25uLuLi4kztDh061OjxX3vtNVn79r6ofmBxYz31zstPyUb4LCkpxcdf/SQwo+pRqVQYf/9IeHmaj+ao0WgwauhATB5/Pxwc7AVkRw1dSUkJtu3ai+9/WYwr12PN1qtUKvQK64IXnnwUnTq048U7IiIiIiKiGnbzt4EB+H3+d0g6F4PvPvsQYXe4yFdaWorf/9yIUZMfR2DH3njjn//FmXMXq9zfgeNnsXDVZrPRT5s1CcQ/XnkWg/v3rvGZGu9GrVZjxOD+eHPWU2azOJaVGTDv9004ce5KneZERERERFQXJEnCB6/LZ2+8dDUWv63eKCij6gtu1wb9eve84zZtWjbHMzMeQjMO+EoCGI1GnDp3Ed/+uhTRh47DYDCYbdMsyB/PPDIRQ/uHw9a28gGIiIiIiIiI6O6aBPpj/5YV2LthCR5/aCKcnRwr3fbC5Wv4x7/+h6BO/TB84uNYumo9ioqK6zBbIhJJkgAVF1jbbfoVzaZYEwV75Y/Rvn17xcesivKFmS4uLjV6fBcXF4SHh9foMcmysLixnmoaFIAZ08bLYnMX/47Y+ERBGVWfnZ0dpo4fAwf7WwWMXp6NMPPRBxHWNYQFY1TnjEYjTp05j29/XoioA0cqvnjXJBBPP/Yghg3qB1tbWwFZEhERERERNSwe7m549vGHcXD7HzgdtRmvv/AkfLy9Kt0+OSUNn30zBx16D0fYoLH4bu4iZGZlV7r9yXOXsWjVJrN47x5d63S2xsr8PYtjWBd5cafBYMSvKzbh/BXzQXmIiIiIiKzdfUP6oVtn+U0g//riJ+h0OkEZVV//vr3Qvm0rs/jNAU36Ycr40RzwlYRIScvA/GV/YtX67cjLLzBb7+LshImjh+KRiaPh5ekhIEMiIiIiIqL6SZIk9O7RFT9/9W8knY3Coh8+w+B+4ZXeN200GrF11z5Me+pV+LbvjadeeR/7Dx0zG7iViIjEq6hQLzZW2X0d5WdtBCqeIdJa3T4LZV3NSEl1RyM6Aao977z8NOYtWYXS0psX70pLdfjPVz/hx88/FJzZvfPwcMdTjz2EYydPw9HBASGdgv+amZIfuKlupaSmYeO23bgRl1DhehdnJwwbGIH2bVux8JaIiIiIiEiQ4Hat8emHb+E/772Grbv2Yv6SVfhz03aUlpZWuP3hY6dw+NgpvPLuf9CyWRM4OTrAyckBTo6OcHJ0gCSpcObSdajVami1WtPSrUtHeHm44MChIze3dXI0/dfOzq7Ovxeq1Wo8OnUc7OxssXf/IVNcX1aGOUvX461npqJxI/c6zYmIiIiIqDZJkoQPX38Wox56zhS7eiMeC39fh8cfHCcws3snSRIeuG84cnLzkJCYDADwbOSBCWNGwqdxI8HZUUNUXFyCXVEHcej4mQoHe1Wr1QgP64y+PbrCxkYLGM23ISIiIiIioprh4GCPaRPvx7SJ9yM2PhGLVvyJBUvX4PK1GxVun5uXj58XLsfPC5ejVfMmeHTKA3h40v0I9Pet48yJiKgywcHBOHPmjKmtdObGivYfPny4omNWVfliw9zc3BqfvdHZ2bnS/sj6sbixHgvw88GTj0zGt3MXm2K//rYKb704E02DAgRmVj2uri7o35dTyZIYxcUl2L0vBgePnqjk4p0KvcJCERHeHTY2NgIyJCIiIiIiovI0Gg1GDhmAkUMGIDMrG8vXrMf8Jatw8OiJCrcvLS3F2QuXqnz8nTt34tP/za5wnUqlgpOTI1xdXDB65FC8/9Zr8PZuXK3zuBcqlQpTxt0Hg8GAqANHTPGSUh0WrdmGl2dMgEqlqvU8iIiIiIjqyvCBvdGzayfEHDlpiv37y5/x8ITRNwuurIhWq8Vj0ybh1JnzUKlUaN+21c0BX41lolOjBsRoNOL46fPYHhmDgsKiCrdp2SwIIwb2QSMPt7pNjoiIiIiIiBAU4Id3XnkG/3j5aew/dAzzl67Bij82Ijcvv8LtL129gXf/8xXe+3g23N1cbg706nhroNe/F8fb/t+0ONj99f/2cHIwX29nZ8uJQIiIqmny5Ml4//33Te2zZ88qOt7p06dl7cDAwDotAnRxcTHNHnn69OkKZ6dU4vbizZo+NonH4sZ67q1ZMzF38e8oLi4BAOj1Jffn3AABAABJREFUevz7ix8x96t/C86MyDoYjUacOH0O23btvcPFuyYYMaQ/Gnlw9gsiIiIiIiJL5eHuhmdmPIRnZjyEs+cvYcGyVVi04g8kJafWSn8GgwG5uXnIzc3D93PmYeOWHdi4einatW1dK/3dTpIkTB0/GkVFxTh68tYof1djk7A75gQGhnep9RyIiIiIiOrK37M3DpvytCl2Iz4Rvy5dg6cfnSQws+rRaDTo0rmD6DSogUpMTsXGHXsRn5hS4Xo3V2cMH9AHbVo25c2rREREREREgkmShPDuoQjvHoqv/u8f+GPjdixYtgbb90TDaDSabW80GpGZlYPMrJway8HB3h6TxgzDZ/98lQPgEBHdo/LFjXFxcYpmPIyKijI7fl0aMWIEli9fDgBYtmxZjRcg7t+/H8DNwsaanhWSxOMw7fWcn483np4+RRZbsGwNrlyLFZQRkfVISk7Fr4tX4I8NWyssbHRzdcGUcaMxbdJYFjYSERERERFZkfZtW+GTD95C7Ml92Lj8V0waOxI2Nja12uf1G7EIHzQSe/ZG3X3jGqBSqfDQZPPvq2t3RCM1I6tOciAiIiIiqiuDI3qiT3f5IB7/+XquaQBYIrqzwsIirN+6Bz8vXlVhYaNGo0H/8DA899gUtG3VjIWNREREREREFsbBwR4PThiNLSt/xfXju/Dvd15G6xZNa73fwqIizF/2B8JHPYQr1+PuvgMREcmUL0CMjo6u9rH+Lv6r7NhVkZubi+XLl+Ozzz6751xu72/Lli2mWRxrQnR0tOl4M2fOrLHjkuVgcWMD8OYLM2Fvb2dql5WV4d//+15gRkSWzWAwYPP2PZizYCniEpLM1ms0avTv0xPPPfEw2rZuwYt3REREREREVkqj0WDEkP5Y/us3SD4fg82/z8OyubMxd/bH+OLf72DIoAHo0qULOnTogDZt2qBFixbo17c3+oT3QEinDmjZohl8vBvDycmxSt8Ns7NzMPT+SViyfFUdnB1gZ2uLhyeNlcV0Oj0WrdkGg8FQJzkQEREREdUFSZLw4RvPymLxiSmYu2S1oIyIrIPBYMDh42fwzS9LcfjEmQpn9mjTsimee2wK+vcOg1arFZAlERERERER3YtAf1/84+WncS5mM6I2LcPMRybBxdmpVvu8dPUGeo2chpjDJ2q1HyIyJ3ExLdaofKHe3zMf3qvNmzfLigmfeOKJe57dMC4uDoMGDcL777+PuXPn4rHHHsOLL75Y5f2Dg4NlszW+++6799T/nfw9w2V4eHiNzwhJlkEjOgGqfd6NPfHc49Pw+be/mGKLfl+Lt156Em1aNheYGZFlOnjkBGIOH6twXZuWzTF8cD+4u7nWcVZERERERERUm9zdXDFsUISpvTPqCC4lZKLlbdsMGxSBMSMGV7i/wWBAUVER8vMLkF9QgPz8AuTk5uK1f3yAQ0dufccsLS3FtBlP43psLN5+7aVaHzCndctm6Ne7B/ZEHTDFrsYmIebYOYR3Da7VvomIiIiI6tKA3t0xoHcYdkUdMsU+/voXPD71AdlAsER0U25ePpb/uRkJSakVrvdwd8WIgX3QqnmTOs6MiIiIiIiIaoIkSegV1gW9unXG7P/7B85dvILcvHzkFxRWuhSYxQrMtqlMekYWBo5/HIu//y/Gjar4mioREckFBgZi8uTJpqLG6OhoxMXFITAw8J6OM2fOHNP/u7i44PXXX7/nXGbNmmU22+KWLVsQHR1d5YLC2bNnY9CgQcjNzcWWLVvw888/K55p8f3330dcXBxcXFzw0UcfKToWWS4LKW6s6Vpp89EEG4bKz/v152bgh1+XoqDw5odKg8GAf33+PRb/+NnNDSRlk3gajdUf7V8S+e+lUovrW8kECQoeb6tWRzMkXrl2wyzm4e6KEYP7o1WLZnWSQ82x1nEoBOJMnPeuglF8672GeM5EdGdW+75grXkrxb/3VFVKXiPinmcVzbJgDX3XdpHfvTAYDNi1/6gs5uvdGCOH9K90H5VKBUdHRzg6OsL7tviuTWswbcYz+HP9Jtn273zwH1y7Hovvv/q01me+GDNyME6fu4iMzKxbecUcR6/Q9goed5F/QxT0rfj1oeS3pIr6bqh/i4mIiIhqxwevPYtdUY+Z2kkpafhp0Uq89ORDArMiskybd0ZVWNio1WoQ0bMrenXrDI3GQm4pISIiIiIiIkXs7GzRpVP7e9/RoJc3DQYUFRUjv6AQyWkZmP7COzhx5oJpfXFxCSY+/go+/+A1vPTUw7w7g4ioCl577TVTUSNws8hw9erVVd5/8+bNOHPmjKk9e/bsauVx+zFuFxUVVeXiRhcXF8yePRuPPXbzd/rPP/8c2dnZ1Sq2BIAXX3wRW7ZsAQDMnz//nos+yXrwl+gGwsvTAy/MfAj/nX2rInvp6g34x8tPoX2blnfYk6jhSb/tZk8A6B7aGUMH9uXFOyIiIiIiogbi7KXryMjKkcWmjLsP2mp8L3R0dMSqJfPwypvv4esffpatmzt/MeLiE7Bi0S9wcXFWlPOd2NnaYtLYkfjh199MscSUDFyJTULLJn611i8RERERlWeE+eAGSm/xEjRYgtJBI5QMbmIsq3RV3x6dMSSiJ7ZFxphi//3mF8ycNhaODvaAUdnjbbxD33cjKRooQxlJyUC3SgfJNegU7KzkeSZwIBEledfhgEU34hPNYsFtWmBov15w/fs76r3ko+i8FY0MrGBfhX0rOufqv58I71sJRc9xhYNAW+tjJpCiQdREvbaUMij4rKDwb6YSigeOkxR8TlHSd7liiXsnaIBCCxqor04JfI5bLWudwEBJ2pLC93CVoPvzlP5bSQryFvqeIrJv6xxkVhmR51xzj9nNAV8d4OjoAO/Gntjz53xMnvkatuyKMm1jNBrx6j8/w7XYBHz5wStQqwVORFNd1voeLirvivq11seQSAAXFxfMmzcP48aNQ25uLs6cOYPPPvusSgWBZ86cwXvvvWdqz549u8qFiBXlUX7mRgAICgq6p+OEh4dj9erVmD59OnJzczF37lycPXsWH330UZWLE6Ojo2UzNs6ePRvBwcH3lAdZF37jbEBefXY6nJ0cTW2j0YiPPvtOYEZElkev1yM7R/5HOaRjexY2EhERERERNSCRB47L2gF+PmjZvEm1j6dWqzH78//gy0/+ZXaj0Zbtu9B3yGjEJ5jfVFqTgtu2glcjD1ls78GTtdonEREREZEIH7z2lKydmp6JHxb8LigbIstUVFSMgsIiWWzy/cMwcfTQW4WNRERERERERHfh4uyEtYu+wePTxput+/aXJRj/+Ktm3z+JiMhcYGAgVq9ebSr+mzt3Lt5///077hMdHW0qIARuFjYOHz682jm89tprZjEXFxeMGDHino8VHByMHTt2mAoto6OjMXjwYDz22GNYvnx5hUWUZ86cwc8//2zaLi4uDuHh4bLjUP3F4sYGpJGHO2Y99YgstuLPzTh19qKgjIgsT2ZWttnog4083MQkQ0RERERERHUuPTMbZy5elcUiwrsrH/0cwEvPP42Vv/0KOzs7Wfzk6TPo2X84UlJSFfdRGZVKhb69wmSxY2cvIze/sNb6JCIiIiISoVe3zhgxsLcs9ul385FfwM++RH9Lz8yWtVUqFVq3qP6gPkRERERERNRwabVazPnfP/F//5hltm7t1j0YOGEmUtIyBGRGRGRdAgMDsX37djzxxBMAgOXLlyMsLAyfffYZoqOjERcXhzNnzmD58uV47LHH8NhjjyE3N9dUGKmksBEAJk+ebJpd0cXFxTQDo4uLS7WO9/eMlPPmzZMVOb7//vsICwtDmzZtEBYWZvr/cePG4fPPP0dcXByCg4NN+1a3f7IunIqsgXn56Ufxzc+LkZObZ4p9+Om3WDn/G4FZEVmO9IwsWdvF2Qm2traCsiEiIiIiIqK6tu/QSdw+5o2dnS3CunSqseOPG3Mfdm3ywf0TH0ZaeropnpCYhA8//hzff/VpjfVVXs+wLli3eQd0ej0AoKzMgOgjZzC8X9hd9iQiIiIisi4fvPY0Nu2MMrXTM7Px7a/L8dasmQKzIrIc6Znya6Iebq5Qq9WCsiEiIiIiIiJrJ0kS3p71BJoE+GLGS++htFRnWnfo+Bn0HfsYjm1bDkcHe4FZEtVPKgAq5WM1W736NOvb66+/jqeeegqbNm3C8uXLMXfuXMydO9dsu/DwcEyePFlxUePtJk+ejMmTJ9fY8YCbeYaHhyMuLg7R0dGIiorC2bNnkZOTg9zcXLi4uCAwMBCBgYEIDw/H8OHDTTNYUsPB4sYGxt3NFS8/8yg++ORbU2z1hm04dvIsunRqLzAzIsuQnpEpa3s2cheUCREREREREYlw/OwlWbtnty6wtbWp0T56du+G/bs2YuS4qbh46Yopvn7TVnz35Sc1MktkRZwcHdA1pCNiDh8zxU6cu8LiRiIiIiKqd8JCgjF6SATWbYs0xT7/cSGenTEVLs5OAjMjsgzlZ270bOQmJA8iIiIiIiKqXx4cPwoBft54YPosZGXnmuKXr8Xh+/kr8PqzjwrMjojIeri4uMgKDePi4hAXFwdXV1dTMaC1CQwMrJXiSaof6lOBMlXRrCcfgbubqyz24WffVrI1UcNSfpRSTw8PQZkQERERERFRXSsqLkFquvx7YffQzrXSV4vmzbB6yXxZLC4+AZcuX62V/v7WPVQ+C2ViSjr0+rJa7ZOIiIiISIQPXnta1s7MysHXc5cIyobIsmRkZcvanh4c8JWIiIiIiIhqRkSvbohavwhNA/1l8S27owVlRERk/f6e1TA4ONgqCxuJ7obFjQ2Qq4szXntuhiy2dvNOHD5+SlBGRJaDMzcSERERERE1XHGJKbK2Wq2Gv593rfXXvl0b+Pn6yGLbdu6utf4AICjQT9bWlxmQmJpRq30SEREREYnQpWNbPDBigCz2xY8LkJ2TW8keRA1Heka2rO3p4SYkDyIiIiIiIqqf2rZqjq/+/aYstu/gMRQWFgnKiIiIiCwZixsbqOefmGZWtPXBJ5y9kRo2o9GI9IxyMzc24syNREREREREDUVsueJGf19vaDWaWutPkiQMHhAhi23fFVlr/QGAg709vDzl33VjE1NrtU8iIiIiIlH++epTsnZ2Th6+mrNYUDZElqGsrAyZ2TmyGIsbiYiIiIiIqKb1C+8GtVptapeW6rDv4HFxCREREZHFYnFjA+Xs5IjXn39cFtu4fQ+OnjgjKCMi8fLy8lGq08linh6cuZGIiIiIiKihiE2QFzcGBfhVsmXNGTygn6y9K3If9Hp9rfZZ/rxY3EhERERE9VWn9q0xcfQQWeyrOYuRmZVTyR5E9V9Wdi4MBoMs1sjdTUwyREREREREVG+5ujije0iwLLZ97wFB2RDVXyrJyOWvhYisF4sbG7CIXt3MYtGHjgnIhMgypGfKZ2200Wrh7OwkKBsiIiIiIiKqa2KKG+UzN+bk5OLIsRO12meQv/y84ljcSERERET12PuvPAlJkkzt3Lx8/LhwhcCMiMRKz8yWtZ0cHWBvbycmGSIiIiIiIqrXBvXtIWvvYHEjERERVYDFjQ1UYWERHp/1jlm8b0/zgkeihsBoNCIq5rAs1sjDXXaxm4iIiIiIiOq39HKzt/j7etd6n76+Pghu11YW27Zzd6326e/nI2uXP28iIiIiovrE08MNLs6Ostjpc5cEZUMkXlpGpqzt6eEuKBMiIiIiIiKq74b06ylrHzt9HukZWZVsTURERA0VixvrE6Oxyssr7/0XZy9cke3+0tOPonOHtpUc/G59lylYDAqWqp9zhYukUrColS0qBYskVX9RSknfSpdaFH3gCK5cj5XFmjUJqKGjSwIXK1VPn2f1lpLHW8l7ODUsSv/m87nWQBgVLtRw8HlSt6z5M6215i2OJEnVXgRnjrIyAwwGgyzqYG9fJ72Xn71x+67IWu3PodyMHDp9Wa32Z1H4nZGIiIioQTEYDJg+633k5ObL4vcN7ScoIyKx8vILEHPkpCzWyMNVUDZERERERERU3/Xo0hGODvJrrjv2HRSUDREREVkqjegEqO6tXr8NcxaukMW6dGyPj999VVBGRGLFJyRhR2S0LObs5IjePTiTKRERERERUUNRUYGfRlM3P50NHtgPs7+fY2pHxxxCQUEBHB0d77BX9Wm1Wllbr9fDaDRaQJEpERERUX1nYYOhGA1336a2KPnsaaz64Bxf/rQYW3bvl8VGD+2HqWNHVG9gs7LSe9/nb5Jawb4KnzcqBX0rpeR5JvI5qmjgO4EDZN0hb4PBgNUbtqOgsEgWb92syV+D/Sl9vAU9ZkoHKVSy/z28H9XovgBg0CvoW+Bjpoiy56hRSd6GBjQwlIygx0zk60Otvfs2tUXBc9So9LOCos8pCvZV6aq/LwBJ0b+XkjkhFP7NVNko2FfBb9aKnydK9hfZtwIiB0NWcs5KXpeiSUpemwoeMyWvLUBc3pb0G0NdstrrWSLzFvc8s7G1Qb9eXbFxxz5TbPveA5g8dngVulby+1H1dyUiIqK6x5kbG5jY+EQ88fJ7spiDgz2WzPkctrYKfjghslLFxSVYuXaTbHYOSZIwbvRwODo6CMyMiIiIiIiI6lL5WRsBQKWqm4uM/fqEywopdTod9kbF1Fp/KpX8J8GbE1bzCh8RERER1S9HTp7DP/77nSzm5+OFX774kAN7UIMUdfA4rsUmyGJtWjRF6xZNBGVEREREREREDcGgvj1k7e2RMbw2SURERDIsbmxA9Ho9Hn72TWTn5Mri33z8Dtq0bC4oKyJxjEYj1m3ebvaaiAjvjmZNAgVlRURERERERCJoNOYjGesrmM2xNjg7O6Fn966y2Lade2qtP71ePnq9Wq0yK3gkIiIiIrJmefkFmPrMO9Dpbn32lSQJi779DzwbuQvMjEiMuIRk7Io+KIu5ODlizLD+LPYlIiIiIiKiWjWkXy9Z+0Z8Eq7eiBeUDVH9I3ExLURkvXjXUgPyf1/+hL0xR2SxyWNHYPqUBwRlRCTW0ROnceb8JVksKMAP/Xr3qGQPIiIiIiIiqq+0t82c+DedTldn/Q8e0E/W3r4rstb6Ki13XtoKCjuJiIiIiKzZi+9+hsvX42Sxt56fjgG9uwvKiEicoqJirFy/DQbDrVkxJEnC+FGD4eBgLzAzIiIiIiIiagiC27SAt1cjWWzbnhhB2RAREZElYnFjA7Ev5gj+9b8fZLGmQf744bN/ciRGapBS09KxaftuWczezg7jRw/nbBVEREREREQNkCRJsLezlcUysrLrrP/BAyJk7ZOnzyAlJbVW+srIzJK1y583EREREZE1+231Jiz4fYMs1qNLB3zw6pOCMiISx2g0Yu3W3cjJy5fF+/fqhiaBfoKyIiIiIiIiooZEkiQM7iufdGTH3gOCsiEiIiJLxAqeBiArOwfTnnkdBoPBFFOr1fjth8/g5uoiMDMiMXQ6HVb+uQl6fZksPmbkELjyNUFERERERNRg+ft4ydqx8Yl11nf3bqFwcnKUxXbu2VcrfZU/L39vz1rph4iIiIiorl29EY9n3/5EFnNxdsSS7/8NrdZ8tnai+u7wiTM4d+maLNY00A99e4YKyoiIiIiIiIgaokER8uLGnVEHUVZWVsnWRERE1NCwuLGeMxqNePKVfyIuIVkW/+CN59ArLERMUkSCRR88gtT0DFmsR9cQtG3dQlBGREREREREZAmC/L1l7bosbtRqtejft7cstn3Xnlrpq/x5Bfk1rpV+iIiIiIjqkk6nx4PPvYu8/AJZ/If/vo1mQf6CsiISJyUtA1t2RctiDvZ2GDdyEFQq3ipCREREREREdWdw356ydlZ2Lo6dOi8oGyIiIrI0/MW6nlu7eSdWrd8qi/XuHoq3XpwpKCMi8ZJT0mVtby9PDBnQR1A2REREREREZCmC/Hxk7bosbgSAIQP7ydrbdu6B0Wis0T4MBgPiyw2CxeJGIiIiIqoP3v/8Rxw8dkYWmz7pPkwdO0xQRkRibduzH/pys2CMHT4ALs5OgjIiIiIiIiKihirAzxttWzaTxbZFxgjKhoiIiCwNixvrua27o81iB4+dxBsffo7snFwBGRGJ5+bqLGu7u7lCo9EIyoaIiIiIiIgsRRN/eXFjTm4ecnLz6qz/wQPkxY1x8Qm4dPlqjfaRlp6J4pISWSyQxY1EREREZOWu3ojHp98tlMXcXJ3xxQevCMqISLzsct9nu3Vuj9YtmopJhoiIiIiIiBq8QRE9ZO0dew8IyoSoflFJXP5eiMh6sbixnhvQp7tZTKfT48sfF6B1zxH4/tel0Ov1AjIjEse7sZesnZyaJigTIiIiIiIisiRejdxhZ2sji52/VLPFhXfSrm1r+Pp4y2Lbd+2p0T7OX7oia7s6O8LNhbN2EBEREZF1O3b6otms59k5eegz9nFs2b1fUFZEYjVyd5O1bbRaMYkQERERERERARjcV17cuO/gMRQWFgnKhoiIiCwJixvruQmjh2He1/+BZyN3s3XpGVl4/q1/IWTAA9iyc6+A7IjE8ClX3Jidk4vi4pJKtiYiIiIiIqKGQqWS0DzITxbbt/9QnfUvSZLZ7I3bd0XW2PGNRiP2ljuf5kG+NXZ8IiIiIiJR+vYIgb+P+YzkZy9ew4hpL+K+h1/CuUvXBGRGJI63l4esnZKWISgTIiIiIiIiIqB/eDeo1WpTu7RUh30HjwnMiIiIiCxF/SxulCRli9FY/QUil4o9OmUsLsZswqvPPgatVmO2/uyFKxgxeSbum/okzl24CBjL7n0p01V7MSpYYDQoXJT8W4skVX9R+vqwWrceAy/PRrIvSACQkpqOaj2eVVqIqFIN8v2IqAGw2s9YRFVRW58Z775IkrKFiKomvFsnWfvK9VjEJybXWf+DB0TI2jv37IVer6+RY1+5dgOJyamyWO+uwTVybCIiIiIikRp7emDfn3MxbuSACtdv3BmFToOm4oV3PkZGZnbdJkckiI+Xp6ydzOJGIiIiIiIiEsjVxRndu8ivTW7fe0BQNkRERGRJzCvdqF5yc3XBZx+8jqcemYQ3P/of1mzcbrbNxu2R2LIrCs88NgX/fO1ZNPJwq/tEieqAWq2Gp4c7UtLSTbGUtHQ0CQoQmBURERERERFZgs7tWsLV2RE5eQWmWGT0QTw44f466X/wQPnMjTk5uThy7AR6hHVVfOzIaPmsjV4ermjTPEjxcYmIiIioAVI6SJWhrMb7buLrhZU//ge79x/Fq/+ajWOnL8rWl5WV4btfl+G3VRvw/stP4NlHJsDGRnuPfVc/b6Oq+pfmJdU95ml2ACWDHgkckEzJ80zpc9RoULZ/tftV+njf2v/mzI232gWFhcgvKICTo8Nd961e16IeM4X9KnhdK9rXoDBvBfsbleQNKHvMFb2uleat5Dmu5N9L5Ljzgl6XgLLnuNJ/67JSBX0reJ4YdNXf92bn1d9VwecMxZQ8ZgoHZlT0+cre4+4bVUbpezgUDGanUt99m8pICvYVTVTuSscOlZT8DVDQudJBTxXtL7JvRR0r3F3Bv7WSc+YAtw2IwH9rxc8z+f6D+vbE/sMnTe0dkQfMtiEiIqKGp37O3EiVatm8CVbN/xo718xHl47tzNaXlZXh27m/oXXPEfjqp4UoLVXwgx+RBfPx9pK1k1PSBGVCRERERERElkStVqN3WGdZ7OCREygqKq6T/v18fdC+XRtZbPuuPYqPm5Obh2OnzspifcM6QqXixUIiIiIiql/69wrFwXW/Yu5n/4CPVyOz9dk5eXjlgy/RcdAUrNu2F0bFxWRElsnD3RU2WnlhbHJqeiVbExEREREREdW+wX17ytrHTp9HekaWoGyI6g+JCxFZORY3NlD9e3fHwa0rMPerf8GnsafZ+qzsXLzy3ifo1O8BrN+6mxf1qN7x9ipX3JjK4kYiIiIiIiK6qU+3TrKiv1KdDtt276uz/gf3j5C1t++KVHzMLTsjUVZ2a9R7rUaNnl3aKz4uEREREZElUqvVmDF5NC7sWY63n38UtrY2ZttcuhaHMY+9iqFTn8fJs5cEZElUuyRJgne5At/k1AxB2RAREREREREBPbt2goO9nSy2Y98BQdkQERGRpWBxYwOmVqsx48HxuBCzCW+/9GSFF/UuXrmO+x96DsMmzsSpsxcFZElUO8rP3JiangGDwSAoGyIiIiIiIrIkbq7O6NyulSy2ddc+xMYn1kn/gwf2k7Wj9h9EQUFBtY936cp17C53UbBrx9ZwdLCrZA8iIiIiovrB2ckR//fG0zi/axkmjx5c4TY79h1C6PCH8dSbHyM1PbOOMySqXeWLG1PSWNxIRERERERE4tjYaNGvVzdZbHskixuJiIgaOhY30s2Lev+YhfPRGzDlgREVbrM9cj+6DByPp1/7EJlZ2XWbIFEt8PaSz1iq1+uRkcmp7YmIiIiIiOimEQN6QaW69dOZwWDAwuVroNfra73vfn3CoVarTW2dToe9UTHVOlZJSSkWr/hDFtNq1BgWEaYkRSIiIiIiq9IkwBdLv/sX9v0xF91Dgs3WGwwG/PzbGrTqMx6ffr8QJSWlArIkqnk+5WduTEsXlAkRERERERHRTYMjesra2yNjYDQaBWVDREREloDFjWTSJNAPS376HPvWL0b30I5m6w0GA+YsXIHuQ6fg7IXLAjIkqjmOjg5wdnKSxVJSeTGPiIiIiIiIbgrwbYzh/eUX1hKTUrBp+55a79vFxRk9u3eVxbbviqzWsf7ctB1pGfLZZ+4bFI7Gjdyqmx4RERERkdUK79YJ0Wt/wcLZH8Lfp7HZ+rz8Arz1n2/RfsAkbN61X0CGRDWr/MyNGVk50Ol0grIhIiIiIiIiMi9uvBGfiKs34gVlQ0RERJaAxY1kJrx7F0RvXIKF3/0X/r7eZuuv3ohD+Mhp2LJzn4DsiGqOT2MvWTs5NU1QJkRERERERGSJhvfriQAf+XfHLTv34nps7V9cGzygn6y9fde9F1VeuHwVu/fJZ3xsHuiLgb1ClKRGRERERGTVVCoVHho/Aucjf8c/X5kJB3s7s22uxSZi1CMv4bMfFnHmALJq3l6NIEmSqW0wGJCWkSUwIyIiIiIiImroOrRticaeHrLYtj0cZIqIiKghY3EjVUilUuGhiaNxYf8GfPDGc3BwsJetz83Lx6gHn8G3v/wmKEMiZQoKClFmKJPFklNY3EhERERERES3aDRqPDx+BFSqWz+hGQwGfP/L4lofIGfwgAhZ+8SpM0hJSa3y/nEJSfhp/lJZTKtR46EHhsjOh4iIiIiooXJ0sMc/X5mJ83t+x8PjR5itNxqNePP/vsETr/0bpaWc6Y6sk42NFu6uLrJYcmqGoGyIiIiIiIiIAEmSMLivfPbGHXsPCMqGyPqpJC5/L0RkvXgnE92Rg4M93n/tWZyLWoewLh1k6wwGA158+z947s1/Qa/XC8qQ6N7k5uZh8/bd+OqHX3H1eqxsXV5+vqCsiIiIiIiIyFIF+nljeH/5xbX8gkJ8/dMCpNfibBc9wrrCyclRFtu5Z1+V9k1KScM3cxaiuLhEFr9vUDi8Pd1rLEciIiIiovogwM8bC2Z/iAPr5yO8Wyez9fOWr8OwB19ARlZ23SdHpIDRaMSFy9ehK3ctPyWNxY1EREREREQk1uB+8uuvO6MOoqysrJKtiYiIqL5jcSNVSaC/L3b/sQCTxgw3W/fDvGUY9eAzyM7JFZAZUdVkZmVj7cZtmP3TPMQcPgad3nyEXR9vLwGZERERERERkaUb0b8n2rQIksWyc3Lxv+/mIim56rMp3gutVov+fXvLYtt37bnrfrHxifjy+1+QX1Agi3ds2xwDe4XUZIpERERERPVKWEh77F3zM/7vrWfN1u2JOYqe983A+UvXBGRGdG8MBgNOnbuEHxf+jqV/bEJevvz7YXFJqaDMiIiIiIiIiG4a1KeHrJ2VnYtjp84LyoaIiIhEY3EjVZm9vR2W/PQZ3nv1GbN12/bsR/ioabhSbiY8ItFSUtOwau0mfDNnPo6ePF3hyC6SJKFDuzYYNXSggAyJiIiIiIjI0qnVajw17QE0DfSVxXNy8/DF97/g7IXLtdLv4AERsva2nXtgNBor3f7kmfP46od5yC8olMVbNwvA4xNHQKXiT4FERERERHciSRLefn46Vs75BA72drJ1V27Eo9foR7F1935B2RHdmV6vx9GT5/Dtr8uwasP2SmdobN7Ev44zIyIiIiIiIpIL9PdBmxZNZbFtkTFikiEiIiLheEcT3ROVSoUP33wev/34KWxtbWTrzl+6hp4jHkTk/sOCsiO6JT4hCUtXrsUPv/6GU2cvVHjzp0qlQkjH9nj28YcxYcxI2NjYVHAkIiIiIiIiIsDO1gbPPTIeAT5esnhBYRG+/Xkhfvv9TxQVF9don4MH9JO14+ITcPHSFbPtCgoLMX/JKvw4bwmKS0pk65oF+OCpB0dDq9XUaG5ERERERPXZuJEDELl6Dvx9GsviObn5GPXwi/hu3nJBmRGZKy3VIebISXw9dwnWbt2NzOycCrdzc3HGuJGD0Dm4TR1nSERERERERGRuUIR89sbtkRxQioiIqKGykLuajH8tt5MUHK7yEewtmuK8FexvNNzT5lMfGIFmgX54YPos2YiPGZnZGDLxCfz46Xt4bMrYqh1MX3RPfctoHau/r2IK/72EPU8VvLYAKD5vRe6cu9FoxLUbcdi7/yCu3YirdDuNRoMuHYPRu2dXuLm61nSS9Yek9LlCZOGs9fMCERFRNUkKPt/daaa4+k3JefPz9L0T+ZhV7d/a0cEOLz0xGd8tXI1rsYmydVEHjuDshcuYNnEM2rdpWSNZtW/XBn6+PkhMSjbFtu7YhTatbx3/5JnzWLJyLXLz8s32b9M8EE9OvQ92thzMh4iIiMgiSKqbS026x2t8FkNU3gZ9lTcNDW6JA2t/xtgn3sThk+dN8bKyMrzwzic4f/Eyvnz/BWg0VbzkrlJwad5GW/19lVL6m4Cif2sFfSu9zqXktBU9ZlXft7i4BAePnUbM0ZMoLCqudHevRu7o0z0EHdq2hFqtrjw/xa9LBedtKFPQrcK8jQr6Nijo+x7ejypiVJS3sr4VPeZK8lb8fiSob8W/rSp5josc817c6wNFudXft0xX/X0d3Kq/L6Dsc6LSx0zJb7MqtYJuFewLKLtvTKXgN1OlLy1Fn1MU7Kv085Gify+lfQt6zJRSkrfIc67p763W0Lfi++Ss9N9aKGvOvYGpxXvbB/ftge9vG0Aq6tBxFBQWwtHBXmGfUP5+Iuq3K6WPt5L3lAZ7XwgREVkCztxI1dazW2cc2LwUndq3lsV1Oj0ef/mfeONfX6KsTMGPw0RVZDQaceHSFcxduAwLl62qtLDRRmuD3j264aWnZ2DUsIEsbCQiIiIiIqJ75mBvhxcfm4Dg1s3M1mVl5+Dbnxdi4bLVSExKUdyXJEkYOqi/LLZ1x24AQFxCEn5ZvAI/zltSYWFjl+CWeGba/SxsJCIiIiJSwM/HC7t//x4TRg4wW/fdglUYPeMN5OSafx4nqk35BYXYHhmDL+csxs6og7cKG8vx8/HC5DFD8ez0Segc3OZmYSMRERERERGRhRjQO0z2XbW0VIfI/UcEZkRknVRcTAsRWS8LmbmRrFVQgC/2rluIh555E+u27pGt+/z7+bh45ToWf/cxnBwdBGVIDcGGLTtx+PjJStfb29mhZ1gXdA8Ngb29XR1mRkRERERERPWRrY0Nnnl4HPYdOoHVm/egtFQ+onvM4eOIOXwcLZs3QUR4d4R0aFf12VzKGTpoAOYvXmZq79gdiY+//B5xCckVbm9vZ4MJI/qhR0g7RTO2EhERERHRTQ72dlj2/b/wzy/m4v++ni9bt2XPQYQ/8DTW/voJWjTxF5MgNRgFhUWI3H8ER06eg76s8hm6mgb6oW+PLmjeJIDfC4mIiIiIiMhiubo4o1fXTth38JgptnXPfowY1EdgVkRERCQCixtJMWcnR6z+9Uu89X+z8b8fFsjWrd2yG33HTMef82cjKMBXUIZUnxUUFFZa2Ojs5ITw7qHoGtIRNjacqYKIiIiIiIhqjkolIaJHCNq3aorFa7bi4tVYs20uX72By1dvwMXZCd27dkbzJoEICvSHu6tLlW4wNRqNCO3SWRYrKipGzMEj8Pc3v3E6uFVTPDhmENxcnKp/YkREREREZEalUuFfrz2Jts2D8MSb/0VJSalp3bnL19FzzJNY9dP/IaJHiLgkqV7T6XT4ZckaZGbnVLpN6+ZN0CcshNfliYiIiIiIyGoM6ddLXty4e7/AbIiIiEgUFjdSjVCr1fjs/VfQtmVTPPPm/0GvvzVS5IkzF9Bj5DT8Mf8r9AjtJDBLqo8KCgvNYu5urujTMwydFcyMQURERERERFQVjdxd8cL0Cdh36ATWbIk0m8URAHLz8rF9d5Sp7eToiKAAXwQG+MHZyRE2Wi3UajX0ej10ej1y8/IRG5+IuPhEFBQWwdPTE+np6ab94+LiZMWNnK2RiIiIiKhuTHtgKJoH+eGBJ99GanqWKZ6RlYMh017CTx+/gekTRwrMkOqr+KTUCgsbJUlC+9bN0bdHKHwaewJGo4DsiIiIiIiIiKpnaP9e+Odn35va5y5dRVxCMgL9fQRmRURERHWNVT9Uox5/cBxaNA3EhCdeRWbWrYsrKWkZGDD+Cfz65YeYMnaEwAypIXh+5qNQq9Wi0yAiIiIiIqIG4u9ZHDu1a4m9B08g6vBJ5OWbD8bzt/yCApy9cBlnL1yu0vEDAwNlxY3x8fEAADcXJ/Tp1gF9unWEs5ODspMgIiIiIqIq6dW1Aw78+TNGz3gDpy9cNcV1Oj1mvPYfnLt8HR+/+TRUKpXALKm+uX22UODmbKKd27dGn+5d0MjDTUxSRERERERERAp169we7m4uyMrONcW2RcZgxtSx4pIiIiKiOscrKlTj+oeHIWbDYrRp0VQWLy4uwYPPvIUPPv8BRo4YSTWkohkpeLGYiIiIiIiIRHBzccLowb3x79eexIzJo9CyaUCNHDcwMFDWTk9Px4QREfjo5ccwon8PFjYSEREREdWxJgE+iFr9I0YNDDdb99mPSzD+qXeQX1D5gCdESrm5OGPM8AEsbCQiIiIiIiKrplarMbhvD1ls2579grIhsk6SxOXvhYisFyuAqFa0bBaE6PWLMDiip9m6j/73I6Y9+xaKiooFZEZEREREREREVLs0GjW6dmyLl5+YjHdeeBRDI7qjdfMg2NvZVut4zZs2gY2NjaltNBqRnJQEtZo/7RERERERieLs5IA/5n6Ml5+YbLbuz6170XfCs4hLTBGQGdVH5Qd85WDCREREREREVF8M6ddL1t4WGYOysjJB2RAREZEIGtEJUP3l7uaCDYu/xaz3PsWPC1bI1i37YzOuxSZgzbyv4OPTWFCGVB9UNHOj0WisME5ERERERERU1/y8PTFmaF8AN7+vpmfmIDYxBXGJKUhOy0BJqQ56vR56fRk0GjW0Gg1sbLTwbdwIQX7eCPJrDA83F1w6dwobtu02HXfb7n2YOm6koLMiIiIiIiLg5uwC/3vvBbRr2RTPvvs59PpbN96dOHsZPe5/En/M/RjdQ9oLzJKIiIiIiIiIyHINLVfcmJmVg6OnziGsUxtBGREREVFdY3Ej1SqtVovvPv4H2rVqhpff/wwGg8G07sDRUxg+9Rkc2LwUtrY2dzgKEREREREREZH1kyQJXo3c4NXIDV07/nUxzmi4805/GTqwj6y4ceuufRzch4iIiIjIQjwxdTRaNPHHhKffQVZOnimenJaB/pOex4Iv38XEcWPFJUhEREREREREZKGCAnzRtmUznL98zRTbuns/ixuJiIgaEJXoBKj+kyQJLzz+INYv+hYuzk6ydSfPXsTv67YIyoyIiIiIiIiIyDoM7d9H1k5MTsWZ85cEZUNEREREROUNCA9FzJ9z0Lp5oCxeXFKKh2Z9hFPnLgrKjOqD8gPbGI1GQZkQERERERER1bwh/XrK2tv2xAjKhIiIiERgcSPVmeEDeyN63UI0CfCTxb/5eYmgjKg+qGiGCl7MIyIiIiIiovqmdctmCCr3m8rW3VGCsiEiIiIiooq0ahaI/X/8hEG9u8riOp0eX/ywUFBWRERERERERESWbWj/XrJ29OETyM3LF5QNERER1TULLm40KlislZJzNgJGgUsVc2zfpjm++PA12VkfOn4aBw4dA8r097wYy0oVLTCWKViUPmYNkaRwaYAkSdxCRERkdQR+1uDf7AZE4fc2K/2uK0lStRdrzftm7goWRa9rZYvS8xb2mNEdSZKEoQPkszdu26WkuJH/XkREREREtcHd1QUbF/wPD48bJosv/WMTUtMyBGVFRERERERERGS5+vXqBq1WY2rr9Xrs3n9EYEZE1kMlcfl7ISLrpbn7JkQ1a/TQCAT5+yI2IckU++aXpegR2lFgVmSNiouLsSfqgOg0iIiIiIiIiOrE0AF9MHfRClN7z/5DKC0thY2NjcCsiIiIiEgZ3nFRpwxlyvav4iA+Wq0Gn73zPJav34nSUh0AoLRUh58XrcA7sx6/526NBt0973M7ZYMPKXyOihr4SOlgs0aDgp0VnHNleRuNsrGvjJUOqKvkvJU+Zgr2V7Kv0te1Qcm/tah9oey8FT9m+urva1TQt5J9AWXnreg9QSFr7btMweOtta3+vgBg51z9fRU9v5W+rhX0LSmc30Cl4BZCg4L3cEnZ3x5JpVaws4K/10ofbyWfFZScs+LvHgIfM2sd8FVU3iIfL6H/1kqeoyKfY9b5/BY5yK3RSicXUfKYWes53wsnRwf06d4Fu6IOmWJb9xzA/UP7iUlIyfuZ0M/wgp4rNf4bBRERNTQWPHMj1VcajQbPTJ8ki/2+fhuSU9MFZUTW6HpsHH74dTFOnjkni9va2ECl4lsbERERERER1T+tWzSVtYuLS1BYVCwmGSIiIiIiuqPGnu6Yev9gWezHhaug0ykoXKAG6VpsAtZvjxSdBhEREREREVGtGtKvl6wdGXNUUCZERERU11gBREI88eADsLO7NdKaTqfHT4tWCcyIrIVer8eWHXswf8lK5OTmma3v1T1U6KhARERERERERLXlwJETsra/rzdcXRSMRE9ERERERLXq+enjZe2E5FSs2bRLUDZkbXQ6HTbvisKCFeuQm1cgW+fq7CQoKyIiIiIiIqLa4eoi/67LAaKIiIgaDhY3khCNPNzw4LiRsthPi1aitFQnKCOyBknJqZgzfyn2HzIfjcXWxgYPjBqGfr17CsiMiIiIiIiIqPbt3X9I1o7o1Y0D/BARERERWbCuHduiV2gHWezbecsFZUPWJCklDXMWr0bMkVNm6+xsbTC03GwWRERERERERNZu3wH5vcHdOrcTlAkRERHVNRY3kjAvzJgiayenpmPl+u2CsiFLl5iUgl8WL0dqerrZuqZBAXj28YfRuWN73tRJRERERERE9Vbk/sOydp+e3QRlQkREREREVfXCYxNk7X0Hj+PY6fOCsiFLZzQaERlzBD//thppGVlm65sF+eGZRyciwM9bQHZEREREREREtePm92F5cWPfHl0EZUNERER1jcWNJEzn4DaI6NlVFvt23lJB2ZClO37qLPR6+RTzGo0GwwZG4NGpE+Dq6iIoMyIiIiIiIqLadyMuAbHxibJYRC8WNxIRERERWbrxI/rDt3EjWeybXzl7I1Us5shJ7Nx3CAaDURbXaNQYPiAcj0wcDVcXZ0HZEREREREREdWO63GJSEhKlcUieoYKyobIukhcTAsRWS8WN5JQzz8un70x5sgpHDp+RlA2ZMncKihenDJuNHp178rZGomIiIiIiKje21tu1sZGHm5o17qFoGyIiIiIiKiqtFoNnnporCy29I8tSM/MFpIPWbYrN+LNYr7ennjq4fHo2bUTr4sSERERERFRvRQZc0TW9mrkjjYtmgjKhoiIiOoaixtJqLHDByDA11sW+/ZXzt5I5kI6todWo5XFrly7ISgbIiIiIiIioroVuf+QrN2nR1eoVPxpj4iIiIjIGjz14BhotRpTu6SkFD//tkZgRmSp/H0am8Um3z8MXo08BGRDREREREREVDf2Hjgma/ftEcoBfoiIiBoQ3gFFQmk0GjwzfaIstnztVqSkZQjKiCyVg4M9unQKlsWOHD+NoqJiQRkRERERERER1Z19MfKZG/v2ChOUCRERERER3StvLw9MHj1EFvtx4Uro9XpBGZGlCgsJhlotv43j8IkzgrIhIiIiIiIiqht7y83c2LdnqKBMiIiISAQWN5JwMx8cB1tbG1O7tFSHOYtXCcyILFV4j1DZrBSlulIcOnpCYEZEREREREREtS81LQPnL12VxSJY3EhEREREZFWenzFZ1o5LTMGfW/YIyoYslZOjA0KC28hih06cQUlJqaCMiIiIiIiIiGpXcmo6Ll2NlcX69mBxIxERUUPC4kYSzrORO6aOHSGL/bRoJXQ6naCMyFK5uboiuG1rWezAkeN8rhAREREREVG9trfcrI1Ojo4I6dhOUDZERERERFQd3bt0QI8uHWSxb35dLigbsmTh3TpDkiRTu6REh8MnzgrMiIiIiIiIiKj27D1wVNZ2dnJE5+DWlWxNRGYkCRIX4Lbf04jI+rC4scZJAheBjMbqLzDihXIjlSYmp2H1xh0AjHdeDHpli6K8FZJUChap+otSCv+tlS1AePeusnQKCgtx4vQ55edFREREJJLIz6VUx0R+ZxTzfVPoD5ciCX1dW+dzRdF3XaWLyN9jqpBf+eLG8O5doNFqFZ6z4sQVLEREREREDVP52RsjY47ixJmLgrIhS9XIww1tWzaVxWKOnoRerxeTEBEREREREVEtioyRFzf26R4CtVotKBsiIiISQSM6ASIA6NKxLfp0D8G+g8dNsW9+WYbJY4aJS4oskq9PY7Ro2gRXrt8wxaIPHkFo5w5QqVivTURERERE1sFoNCIrJw+xiSmITUhGRlYOSkt10OnLYDAYoNFoYKPVwN7OFgG+Xgjy84G/rxdstFrRqZMAe/cfkrX79goTlAkRERERKWNhgz1ISq6rGJT1ba0DNinJu0yHiSP64XWvRkhOyzCFv/l1KeZ++o8772tQWNSmtqn+vgoHR5Gk6t+MaFTyHDUqfMwUPUcV7GssAwD07tYR5y5dMYXz8vNx6twFdOnQVkFed+tb4evSqOR9QUnfCt+PlOxvqP6+RsXvg8qfZ9VmKFXQt4K8lb4XKtlfr6v+vmUK9gWUvbYU/a2HstyV5K12r/6+AKBScDuckn0VvQ8CKFPw2lQp/WymZH8FzzMl7ycAjKUF1d5X0jhUv2ML+khfp5R8LhQ9IKVVUvKYKR2MUsHrWvG/tajnmXU+R4UP9nqPjEYjDAYDysoMUKtVvN+zTol8D7/7/nvLFTf27dm1SvtRTbPS3+uIiKheYHEjWYznZ0yRFTdGHz6BIyfPomun9uKSIovUu2c3WXFjZlY2zl+8gvZtWwnMioiIiIiI6M7iElNw/Mwl3EhIRmxiCvILCu9pf5VKgo9XIzTx90GLJgEI7dgGdrYKblAlq5CTm4fjp87JYn17dROUDRERERERKWFjo8WT08bio69+McWWrNmCT95+Do3cXQVmRpYmwM8bTQP8cD0+0RSLOnQCIcFtrO4GZiIiIiIialh0ej0Sk9NNg7zGJqQgLTMbJaU62SAfarUKdrY28PP2QpBfYwT5eSPI3weeHm5Qqfi9p6HIys7FqXOXZLG+PUIFZUNERESisLiRLMYDIwbA37cxEpJSTbHPvluApT/+lxdoSKZZk0D4ejdGUsqt50rUgcNo16YlnytERERERGRRdDo9jp6+gMgDx3AtLknRsQwGIxJT0pGYko79R09j5cad6BESjL49QuDn7VlDGZOliT54VHah18ZGi+6hnQVmRERERERESjw1bSz+8+186PU3Z2cqLinF3KVr8eazDwvOjCxN77DOsuLG9MxsXLxyA21aNhWXFBERERERUQXyCwoRfeQ0jp66gISUNJSV3X1W4LIyAwoKi3HpWhwuXYszxe1sbdAsyA+9QoPRuV0raDTq2kydBIs6dEx2LdTOzhbdOgcLzIiIiIhEYHEjWQytVounH5mA9z753hRbsW4bunRshzefny4uMbI4kiShT88w/P7nBlMsISkZ12Pj0axJoMDMiIiIiIiIbsrIykHkgeOIPnIKBYVFtdJHcUkp9hw4hj0HjqFVs0BE9AhBSPvWUKtVtdIfiREZfUjW7h7aGXZ2toKyISIiIiIipXy9PTHpvkFY8sdWU+zrX1dg+sRR8PbyEJgZWZqWzQLRuJEHUjMyTbGowydY3EhERERERBbBaDTielwS9hw4jqOnL5gG8VGquKQU5y5dx7lL1+Hs5IDe3TqhT7eOcHdzqZHjk2WJjDkqa/cM7QhbWxtB2RAREZEoLG4kizJz2jj895t5shs/3/7P1/D19sQjE+8TmBlZmnZtWsLdzRVZ2TmmWFTMYRY3EhERERGRUGVlZdiy5wA27d5fpRFJ7e3tEBTghwBfH9jb20Gr0UClkqDTl0Gn0yEjKxtx8YlITk2XjVhZ3t8jmgb4eOHh8SMQ6Oddk6dFAu2LOSxr9+0VJigTIiIiIiKqKc9PnygrbkxKTceoR1/BrhXfwdnJUWBmZEkkSULvsM5Ys3mXKRabkIzYhGQE+fsIzIyIiIiIiBoyo9GIgyfOYee+w4hLSq3VvvLyC7F5dwy27DmATm1bYFi/HmgSwO9D9cnemCOydp8eoYIyIbJeknRzaej4GBBZNxY3kkVp7OmBnz9/Hw8++7Ys/sSrH6GxpzuGD+gtKDOyNCqVCuHdu2LD1p2m2OVr15GSmgbvxl4CMyMiIiIiooYqPjkVi1ZtRlxiSqXbuLm6oGtIBzQNDEBQgB88G7lDqsIvrCUlpYhPTEJsfBLOX7qC0+cuVljsGJ+chk9+WIzh/XtieL+e0GjUis6JxCoqKsbBoydlsYhwFjcSEREREVm7Hl2CMSC8K3ZF37qB7+jpC5jw1D+wbt7nsLHRCsyOLEmHti2wM+oQcvLyTbGoQ8cR5D9cYFZERERERNRQZWTl4Lc1W3H+yo27btvYqxGCAvwQ5O+HAH8fODo4wEarhUqlgl6vh06vR1Z2DmLjExEbn4i4hCTk3vbd53ZGoxEnzl3GyfNXMKh3V9w3qDe0Wt4Cb+0KCgtx5OQ5WSyiZ1dB2RAREZFI/GRHFmfK2GFISk3Dqx98YYrp9XpMnPkGdq6cg7CQYIHZkSUJ6dgeu/fFoKCw0BSLOnAE40bzYh4REREREdWdqszW2LZVc0SEd0fH9m2gVt97waGtrQ1aNGuCFs2aYEDfnsjMysa+mMOIOnAEefkFsm0NBgM27ozGybOXOIujlTtw5AR0Op2prVKp0KtbiLiEiIiIiIioRkiShN++/hC9H3gS1+ISTfFtew/i8df/Dwu+fB8qlUpghmQp1Go1eoZ2xJY9+02xC1duIC0jC16N3AVmRkREREREDYnRaETUoZNYvXkPiktKK9xGo1YjtHMH9OwWgiaB/rC3t7vrcYMC/NC5QztTHzm5ebh45Rr2xRzG5avmBZRGoxHb9x3GqQtX8fC4YWgW6KfsxEiomCOnoNfrTW2NRoNe3ToJzIiIiIhEYXEjWaSXn3wIiclp+N+Pi0yxgsIi3Pfwi4haOw8tmwUJzI4shVarRfeunbFr762LeafPXcDAiF5wc3UVmBkRERERETUUufkF+GHhatxISDZbJ0kSInqFoX+fnvBu7Fmj/Xq4u+H+EYMxckh/HD91Dpt37EFicqpsm79ncRw/cgAG9Aqt0f6pbuyNOSRrd+nYHi4uzoKyISIiIiKimuTTuBE2L/4KvR94EumZ2ab4b2u2wLexJz5953lxyZFF6dqpHSIPHEVRcYkpFn34BMYM6y8uKSIiIiIiajDuNltjI3c39A0PQ6+wUDg7OVa7H0mS4Obqgu6hndE9tDMSk1IQuf8QDhw5jpJyBZUpaZn435xlnMXRykXGHJG1Qzu2haODg6BsiIiISCQO90gW65N3Z2HauBGyWFpGFoY/+DxS0jIEZUWWJqxLZ2g1WlPbYDAg5tAxgRkREREREVFDkZGVg//NWVJhYaOvtxdef+FJTB53X40XNt5Oo9GgW5eOeOulpzFicD+zmT0MBgN+X78D63dEwWg01loeVDv2RsuLG/uGhwnKhIiIiIiIakOrZoFYP/9zOJSbzeLzn37DV3OXCcqKLI2NjRZhnYNlsZNnLyM3L19QRkRERERE1FBci0vCx98tqrCw0c3VBTMfnYIP334JQwf0VVTYWBE/X29MGXcfPn7vdYwePhAatVq2/u9ZHL/6ZQUKCotqtG+qG/sOHJW1+/bsKigTIiIiEo3FjWSxVCoVfvniAwzu20MWv3ojHqMeegF5+QWCMiNL4uBgj9ByF/OOHD+NQn5ZJSIiIiKiWpSakYX/zVmCtIxsWVySJAwb2BdvvfwMmgb511k+Go0Go4cPwhsvPgk/X2+z9Rt3RmP1pt0scLQiOp0O0eUG7+nbs5ugbIiIiIiIqLZ0DwnG7z/+B+pyN2m+8tFsLFu7TVBWZGm6dwmW3chbZijDgWOnBWZERERERET13fkrN/D1rytQWFRstq5X91C899rz6NKxvdngqzXNzs4WIwb3x1svPY2gAD+z9dfjk/DVLyuQwwFgrEppqQ77j5yUxSJ6hgrKhoiIiERjcSNZNBsbLVb98jlCO7aVxY+eOo8JT7yO0lKdoMzIkvTqHir7gqzT63D42Mk77EFERERERFR9WTl5+PrXFcjOlV8g82zkjtdfeBJjRg6BVqMRkltQgB/emvUUhg7sa7ZuR9RhbNgZLSArqo5jJ8+aDdzTh6OVEhERERHVSyMG9MLPn75tFn/05Y+wM+qwgIzI0jg5OiAkuI0sdvjEORQXlwjKiIiIiIiI6rNzl6/j+wWrUVLuHl03Vxc898TDeHjSWNjb29VpTn6+3nj9hZm4f8Rgs1kcE1PS8eXc5cjJ46Qp1uLwiTNm32l7h3URlA2RlZMkLn8vRGS1WNxIFs/ZyREbFn+D5k0CZPFtkTF4/KV3YTAYBGVGlsLN1RUd2skv5h04chw6HYtfiYiIiIioZuUXFuHreSuQmZ0ri/v5euPV556o09kaK6PRaDB25BBMGTcaUrkfbzfujMbO6COCMqN7Ebn/kKzdvk1LeHk2EpQNERERERHVtukTR+H/3nhaFtPp9Hhg5ps4fuqcoKzIkoR36yT7nl9SWorDJ/ncICIiIiKimnU1NhE/Lf4D+rIyWbx9m5Z477XnEdy2laDMALVajeGDIvDGrKfg5uoiW5eWkY1v56+scKZJsjx7DxyVtTu2awUPd1dB2RAREZFoYqYRILqd8e7Fid6e7ti0+Gv0HjMD6ZnZpvhvqzbAt7EnPn3vpWp2ra/WfgAgSQprg1Xqu29T+c7V39VYdvdtLJax0jXh3UNx8syti3cFhYU4fuoMwkI7/xUROBoDR4Igqpyx8tf1XYl8bSntW8l5E1EtUvLattbXtcjPKSIfM34+u3cN9DGr4G/+0j+3ISUtUxYL9PfFi089CkcHh7rKrEoiwsNgZ2eDBUtXw3jb549VG3eheZAfmgb4mu8k9PuLdX6+kxS+n1X20XBvueLGPr3CUH9ei0rPw1r/7hIRERFZGEWf/xVeJ1P0G6mV/q5cheuSbz3zIBJTUvHdgtWmWF5+IUZOfRpR6xagWTUH1DGqq399UFIpfMyUXBM1KLmeWv3rwDcpeJ7V0vVYD3cXtGvZFGcvXTPFYo6eRM/QYGg0f936oeS1VYXr9nfeX9B3NaX9KtpfwWNmUPg8UbJ/mcJBgguzq7+vRlv9fcsUPmZKzluvYJZUJfsCgF5B3mol76MAtPbV31fJPTVKzhkA1ApemyqBt9Ip/fdSQsnfa5WNgn0VPt6Sks8ZSj8riKLks7DCz/BK97fGvkXeG6Kkb2u+1qMod3HnXX6wUWuWmp6F7xasQqlO/j4Z2rkDpk8dd+u7h2ABfj549bkn8PWc+UhLv3XtNjElHT8sWoOXHp8Etci/rRUwirxfzAJfW3tj5MWNfXuG1nDfVno9kfclEhFRA8WZG8lqtGoehA2LZsOh3FT2n/+wAN/+ukxQVmQpfLy90LJZE1ks+uBRzuxJREREREQ15sjJ8zh2+oIs5t3YE8/PfMTiChv/1j20M6aMu08WMxqNWLRqM3R6a715o/4zGAzYFyOfYTOiV5igbIiIiIiIqK5IkoSv3n8RE0b2l8WTU9Mx4sFnkZ6RJSYxshi9wzrL2vkFRTh57rKgbIiIiIiIqD4xGAxYuGoTiorlgzN0DemAGdMmWExh498aebjhlWcfh1cjD1n8amwitkYeqmQvsgRlZWXYd/CYLNa3R0XFjURERNRQsLiRrEpYSDBW/vwpNBr5iCpv/PsrpKRlCMqKLEXvnt1k7azsHJw5d1FQNkREREREVJ/k5Rdg+dptspijgwNeePJRODs5Csqqavr2CsOIwf1ksaTUdGzcGS0oI7qbM+cvISs7Rxbr26tbJVsTEREREVF9olarsfCLdxHRXV7EdvHKDYyb8QoH9mzg/H0bo1mgnywWffik2BlAiIiIiIioXtgVfRRXYxNlseC2rTB96nioVJZ5u7mrizNefOpRuLo4y+Kbdu9HQnKaoKzobk6du4TcvHxZjMWNREREDZtlftokuoPhA8Lx82fvyWLFxSX46uffBGVElqJpUAD8fBrLYuu37ERScqqgjIiIiIiIqL5YtnY78guLZLHJD4yCh5uroIzuzcgh/RHo7yuLbY08iOvxSYIyojuJjD4oazcNCkCgv18lWxMRERERUX1jZ2eLP37+GB3aNJfF9x08hj837xaTFFmM8rM3pmdm48QZDvhKRERERETVl5KeibXb9sliXo088MTDk6FWqyvZyzI08nDHU9MflBVglpUZsGjVZpSVlQnMjCoTGXNE1m7ZLMjs3l8iqjoJgCRxkUT/QxCRIixuJKv06KT7MH3SaFnshwW/IzsnT1BGZAkkSUKfnmGyWElpKRavWIPMrGwxSRERERERkdU7euo8jp2+IIuFdGyPriEdBGV079RqNR6e/IDs4qPRaMSiVZug1/OinqXZF3NY1o7oFVbJlkREREREVF+5uTpj04LPEegnv7nvv9/8wln6GrgWTQPg7ekhi63bthdXb8QLyoiIiIiIiKyZwXCzEFCn15tikiTh4cljYWtrIzCzqmsa5I8h/XvLYnFJqdgaeUhQRnQne2OOytp9enQRlAkRERFZChY3ktV687lHIUm3auxz8/Lx/YIVAjMiS9CuTUu0b9NSFisoLMKi5auRV24aeyIiIiIiorsxGIxYu3WvLObo4IAp4+6TfSe1BgF+PhgxuJ8slpSagUMnzgrKiCpiNBoRGS2/0NqnVzdB2RARERERkUj+Pl74+M2nZbFDx89gx94DgjIiSyBJEiJ6ym/8LDMYsOzPbUhMThOUFRERERERWavIA8dxNTZRFuvfpwdaNm8qJqFqGjl0AHy9vWSxTbv3IzktQ1BGVBGj0Wg2c2NEz66CsiEiIiJLweJGslptWjbF+FGDZLHZP/+GwsIiQRmRJZAkCeNGD0ezJoGyeFZ2DhavWIPi4mJBmRERERERkTW6cOUGUjOyZLHJD4yCi7OToIyUGTawLwL9fWWxyAPHxSRDFbpy7QaSUlJlsYhe3QVlQ0REREREok0aNQDNmwTIYv/95ldB2ZClaN+6OcI6t5fFSnU6/LZmMzKyssUkRUREREREVsdgMGDbXvmgm16NPHD/8MGCMqo+rUaDhyePg0p169b4sjIDdkYdvcNeVNcuXrmBtHLX31ncSERERCxuJKv21vMzZO20jCz8uuxPQdmQpdBoNJgy7j74ejeWxVPS0rF01VrodDpBmRERERERkbWJPHBM1vbzaYyuIR0EZaOcWq3GqKEDZLEbCcm4Hp8kKCMqb+/+w7K2d2NPtGrRVEwyREREREQknEajwevPTpfFdu47iANHT4lJiCyCJEkYMTAcwa2by+IFhUVYtGoT8vILBGVGRERERETW5PSFq8jKyZPFpk0cA1tbG0EZKdM0yB8D+/aSxQ6dOIui4hJBGVF55Wdt9PPxQrMgf0HZEBERkaXQiE6ASInQTu0wrH84tuyONsU+/2EBnnp4PLRarcDMSDRbW1tMmzgGv/62AplZOab4jbgErFq7CZMeuE82Qg8REVFtMxiMSMvIRGxCsmnJyMqBTq+HTqeHwWCEVqOGVquFg4MdAnwaI8jfB0EBPgjw9Yadlf5wTERkzTKzc3Hy3GVZLCK8OyRJEpRRzejQrjU83FyRmX3ru1LkgeNoGuB7h72oruzdLx8dt2/Pblb/nCMiIiIiImUenTQaH33xE5JS0kyx/379C9bM/0pcUiScSqXCAyP6o6i4BFdjE0zx7Jw8LFq1CY9Nug/29nYCMyQioobMaDQiO78Emfkl0JcZoNMbYIQRGrUKWrUKTnZaeHk4Q817d4iIhIo8cFzWbhLoj9Ytm4lJpoYMjOiFnXv3w2AwAABKdXocOHYG/XuFCs6MAGBvueLGiJ5deS2UiIiIWNxI1u/tF2fIihtjE5KxZM0mPDrpfoFZkSVwcnLEw5PH4ZdFy5FfUGiKn790Bes2b8f9I4bwSxEREdWqwqJixBw5hRPnLiEuIRnFJaV33L5UpwOKipGdm4fE5DQcPH4GACBJgLdXI7RsGog+3UMQ5O9TF+kTETV4UYdOwGg0mtq2tjboHtpZYEY1Q6VSoU+vbli7aYcpduTkeYwb0R9Ojg4CMyMAiNx/UNbu26u7oEyIiIiIiMhS2NnZ4uWnHsIbH31piv25ZTdOn7+MDm1bCsyMRNNoNJh8/xAs+H09ElPSTfHU9Ews/XMrHh4/ElotbwshIqLal5VXjNi0vJtLah7i0vKRX6y74z5ajQoBns4IauyCQK+b//Vxd4RKxXt5iIjqQmpGFs5eui6LRYRb/3UpN1cXdO7QDsdOnjHFIg+eQL+eXXi/qAXYe+CYrN23Z1dBmRDVH5Ik8f0N4GNAZOX4KzZZvb49QhHerTOiD58wxT75dj4ensCZ+Qhwd3PFw5MfwLzfVqK4pMQUP3byDBwdHDC4fx+B2VFdKy3VIS4pBbEJyYj7e8Y0nR6lej2MBiM0GjW0Wg0c7Ozg73tzxrQmAT5wd3Xhh14iuiexCcnYE3MUh0+chU6nV3w8oxFITs1AcmoG9h28ObNWRM9QhHZqCxvOVk1EVCsMBiOiDp+UxXp0DYGdna2gjGpWePeu2LB1N8rKygAAOr0eB4+fxcDe3QRn1rAlJCbj6vU4WSwiPExQNkREREREZEmeengCPv76F2Rl55pin347Dwu//T+BWZElsLW1wbRxI/DrsrXIyMw2xWMTkvH7+u2YfP8QqNVqcQmSUEUlOhSW6KDTG6AvM0CSJNhIOmjUKrjY20Ct5j0VRFR9xaVlOHQ5AfvOJCIho+Ce99fpDbiWnINryTmmmLO9DXq190OfYH94uNjXZLpERFTO3nKzNjrY26NrSAcxydSwiPAwWXFjSlomLl6NQ5sWQQKzohtxibgRnyiLRfTkjJpERETE4kaqByRJwtsvPo7Rj7xoip2/fA1/bN6FcSMHCcyMLIV3Yy9MHX8/Fq1YA73+VoHJvphDcHSwR6/uHPmlviorM+D0hcs4cfYSYuOTkZSaLpt5505OX7hi+n8nR3sE+fmgRbNA9ArtCDdX59pKmYismMFgxJGTZ7Er6jCuxyfVal/X45NwfeUGrNqwA726dcKgvt3h6uxUq30SETU0qRmZyM2T34zRt1f9KTJzcXZCl47tcfj4KVPs8vU4FjcKtjfmsKzt6uKMDu1aC8qGiIiIiIgsibOTI56fMQX/+mKOKbb0j8348I1n0SzIX2BmZAkcHezx8PiR+GXpn8jLv/V7xsWrsVi3bS/GDOvHgTwbgPyiUsSl5SI2JQdxqbmIS8tFRm5Rpdtr1BL8GzkhqLEzgrycEejlDF93BxY8EtFdJWUWYu+ZZBy8mIZiXVmNHjuvqBRbj1zHtqPX0aGpJ/p2CETbIA+o+HeMiKhGGY1GHDpxThbr1b1LvRlgu3WLZvBu7ImU1Fsz3B88cZbFjYLtPXBU1vZwd0W7Vs0FZUNERESWhMWNFVHyY0gVi2ZqvN+bB6j+rgrSvtm1oL71N3+IH9mvKzq1a4mT5y6bVn08ey4eGNLrzhdpVApGqFQr/BKn5LkikpX+WNgkKAATx4zE8jXrYTAYTPEtOyPhYG+Pzh3bC8yOalpuXgGiDp/AvgPHkZWTe/cd7iK/oAhnL13D2UvXsGH7XnRu3xoRPbugdfMmvBAsksj3UWt9D7dmIl9rVfj3Ts/MxuLVm3Dxauxdt3V1cUagvy+CAvzg7+cDeztbaLVaqCQJOr0eOp0e6RlZiE1IRGxcApJS0mR/u25XUFSM7XsPIvrwSUwcPRjdQ4Jve19S8pjxOU7WgH+D61QD/MwTm5gia7u6OMPf11tQNrWjXesWsuLG2ISUO2xdF5T8/akfz9G9+w/J2n16duPsGhWqH//eRERERET36sXHH8QXPy5CQeHNa6RlZWX47Pv5+P6/7wjOjCyBm6szHho3HPOWr0dxSYkpfvzMRTg62GNIRA+B2VFtKSzW4cC5BESfiUdSZv497asvM+JGah5upOaZYlq1Ch2aNkLfDn5o5efGa6FEJJOYWYCVUddxMSHn7hv/RatRwU6rhlatgiQBujIDdHoDikrvXBRpNAKnrqXj1LV0eLraY0yvlujSsn79Rk9EJFJmdi5y8wtlsfB6NEmEJEkI7x6KNeu3mmLX45MFZkSAeXFj3+6hUKk4uAoRERGxuJHqCUmS8NZzD+PB5/9pih05dR7b9x7CkIjuAjMjS9KmVQuMHj4If27cJov/uWkbHBzs0apFM0GZUU25FpuAXVGHcezMBZSVVVwIpJTBYMSx0xdw7PQFeHs1QkSPLggP6wRbG5ta6Y+ILJvBYMTeg8fwx+bdKCnVVbiNSqVCl07t0S2kI5oE+sPN1eWe+ijV6ZCQmIzzl65gX8wRZGWbXywsLCrGghXrcezUeUwdOxyuLpzFkYhIqfKFfoH+voIyqT2BAX6ydmZ2LvILi+DkYC8oIypf3FifZgslIiIiItwcOEayoBu2jApm2VFa66JkAFKDkrwVPv5GBdcelBQIldz8TbCRo4SZU+/DV7/8blo1b+mfeP+5qfBp3Kjy/VUKbguwEfhbo9DXi6CCLoWDK3p7uuPBsUOwcOVG6MtuvVaiDh2Ho70twrt1qrW+FVHy2hLIqOQxU/IeDCAuORORp+Nx+GIydPqae/x0ZQYcu5KGY1fS4O1mj77BPujRujHsbW97H9GXVH6AuxG1r1KlxeL6VvL6yMtS1reNQ/X3VTJguEFf/X0BZXlDwfNMrfCeAaGDnN35716ZwYhtx+Kx6UgcygyVv/fZ26gR6OWEIC9HBHk6IdDLCZ4uthUWSucV6RCXlo/YtALEpecjNi0fWfmlFR43PacIv2w+hS6t0jCpf3s4O9jezFpjew/nWI6S9+FKBqatOgWfcZS8PpQWjyj5bKa0WF5S8HlWreQWWaV5K9hf5AADQvMWc96SJX1XryPlr4U62NvDp7GnoGxqR4um8lkaU9IyUVKqg62NkolNrHSwcEXfuWrunPfGlCtu7BlaY8c2o/T9yFonPxA1wRMREZFCLG6kemPCyAF4r8kcXLmRYIp9/N1CFjeSTJdOHVBQUITte/aZYgaDASvWbMAjU8aZ3dxL1qGouBirN+5C1KETd91WkiT4NPZCUIAfAvy8YWdnBxutFpJKgl6nR6lOj4zMv2ZMi09EUVHlF6pS0jLw+/rt2Bl1CA+NH4k2LZrU5GkRkYW722yNbq4u6NurG8K7d4Wri3O1+7HRatGsSSCaNQnE0AF9cfrcRURGH8S5i1fMtj157jIuX5/71yyOHTiiMhGRArEJ8pE7g+rhdwVfby9oNRro9LduhohNSEb7Vhz4RYSMzCycPndRFuvbq5ugbIiIiIiIyFK98sQkfLdwDXS6m9/lSkpL8eUvv+OTt58WnBlZiiB/H0waPRjL1m6D4bbii62RB+Bgb4eQ4NYCsyOlLsSmY93+S7ienH3P+6ok4A41SWZSsouwMuoa1h64gZ5tGmNUWBAc7ZTcCE5E1igxswCLdl1CXFpBheslCejQxB19g33QNsANqipen3S216J9kDvaB7mbYkmZhdh7JhkHL6ahWGdefHjsUjIuxWdi8oD26NKq/g1ISERUl2IT5cWNQQG+9e4ekwA/H0iSZBqUxGg0Ij4pFS2a+AvOrGFKTc/A+cvXZLFaLW4kIiIiq8LiRqo3NBoN3njmITz11iem2O79R7H/yCn06tpRYGZkaXr37IaCwkLsP3RrFBidXoclK//EY9MmorFX/RqBqL47e/EqFq/ehOycvEq3aRLoj7AuHdEkKAABvj6wta3aiIlGoxHpGVmIjU/E+UtXcOjoSZTqzGdmy8jKwey5SxHRMxRjh/eHXRWPT0TW6+LVWPy4aBWKS8xHD3V3c8X40cPRuUNbqGt4hFW1Wo3OHdqhc4d2SElLx/otO3Hk+GnZNn/P4nj5Whymjh0OldIROImIGiCD4eaFrds1Cax/F7nUajUC/H1w7Ua8KcbiRnGiDhyRte3t7dC1cwdB2RARERERkaUK8G2MR8YPwy/LNphiP/72J956dhrcXas/yBrVL62bB2HM0Ais2bxbFl+7bS8c7O3QunlQxTuSxSoq0eGPfRcQdTrujttp1Sr4N3IwzZwW6OkETxc7aDUqqFU3b+zWlxlRVKpHQkahaba0uLQCZORVPGNdqd6AyDPJOH4tA1MiWqATL6cTNQgGoxHbjiVg4+HYCmdrdLbXolfbxujT3hseznY10qevhwMm9W2O+3s0waFLadh7JhmJmYWybfKLSvHLxuMIbZWMqUND4WDH+zOIiKqj/MyN9XGgVxsbG/h6eyEx+dZ139jEFBY3CrLvwDFZ29HBHl06tBWUDVH9IklSvStQrw4+BkTWjcWNVK88Mn4EPvzyFySmpJti//1uEf789VOBWZGlkSQJQwdGoLCoCCdOnzPFi4qLsXjFH5jx0ES4uboKzJCq4m6zNWo1GnTr0hF9e3VH06Dq/SAhSRK8PD3g5emBriEdMO6+YYg5chyR0QeRkpputn1kzFGcuXCFszgS1XMnz13G3KV/QK83Hy20T89ueOC+obC3q5kLeHfi7eWJxx+ahNDOHbBs1Trk5ctHS406dAKFRSWYPnk0tBp+7CciuhfpWdlmBez18YIeAAT5+8mKG+MTU++wNdWmyP2HZO1e3brAxoY35hARERERkbk3nnoQ81ZsMs3Kl5dfiO8WrsG7LzwiODOyJJ3bt0JBYRG2Rh4wxQwGA1as245HJoxEkL+PwOzoXpyPTcdv208hK6+4wvUqlYSQZh7o094HLXycoVZXPuihJEnQaiRoNTZwcbBBu0A307q8Ih2OXE7H3jPJSMkuMts3t1CHOZvPI6yZCyZ0awxH25od4JGILEeZwYjFuy7h0KU0s3WSBAzq7IdR3YKg1dTOIKt2Nmr0DfZBn/beOHw5Hb/vu4bCEr1sm6OXkpGctQ/Pjw+Hq1PtX5slIqpPjEZjBTM31tNroQF+suLGuHJFnVR3ImOOytq9w0Kg4f1MRERE9BdO40L1iq2tDV55cqostm77Ppw6f0VQRmSpJEnC/SOGoFVz+YwkuXl5WLz8D+j1+kr2JEuQlJqO/5v9a4WFjba2Nhg7agj+8/5reHjyA9UubKyIvb0dBvTpifdffwGznp6Ols3MCxj/nsVx/ba9MBrNRy8kIut27PQFzPlttVlho7ubK1548hE8OOH+OilsvF2Xju3x3uvPo1uI+UzVx06fx8+LzfMlIqI7KyyU3yim1Wrh6lI/Z7/w9PSQtQuLK75JjmpXYlIKFq/4Qxbr06ubmGSIiIiIiMjitWoWgIkj+8tiX89biYJC82IkatjCu3VC726dZDF9WRmW/LEVqemZgrKiqiop1WPpjtP4ds2hCgsb3RxtMCosEP+a1hUzhrRBa3/XOxY23o2zvRb9O/ri3ckheGF0MEKae0BVwaQHh67l4v/WXcOp+Pxq90VElkunN+CXrecrLGz0drPHq2M7YmzPprVW2Hg7SZIQ1soL704OQaemHmbrE9Nz8cWySKTnFFSwNxERVSa/oNDs+2Ogf/0sbgwsV7SZlJYhKJOGzWg0Yne0fKDXPj1CBWVDRERElojFjVTvPPngGHi4uchin3y/SFA2ZMnUajUmPTDK7It5emYmzp6/JCgrupvYhGR88dNvyMzONVvXtlVzvPvq8xg6oC8cHRxqLQdJktCmZXO89MxjmDh2JGy0WrNtNu6Mwop122AwsMCRqL44d+ka5i1fZ/a6DunYHu++9hzatW4pKDPAydERMx6aiBkPTTQb1ez0hStY8Ps60yjuRER0d6U6naxto62/I0ZqNfLPsjodB3qpa6WlpZg04wWklruYOqR/H0EZERERERGRNXjzmQdl7fTMHMxdtkFQNmTJBvftjpD2rWWx4pISLP1zG8rKODCepcorLMFXqw4g6nSc2TobjQqT+jTDh9O6YkTXQLg62tRo35IkoY2/K54Y2hYfPNgVwUHuZtvkFpfhp90J2HGWRbJE9UlZmQG/br+Ak9flr21JAgaH+OGtCZ3R1LvuBwJ0cbDBzGFt8OigVnCwlf9en55TiNkr9iErj4M8EBFVVUmpzizm4uwkIJPa5+IkP6/SCs6dat83vyzBybMXZbGInl0FZUNERESWiMWNVO84OTrghccmymLL1m7H1RsJgjIiS6bVatG/T88K42R5rsUm4Kufl5iNHGVra4Op40fjhScfRSMPtzrLR6VSYUCfnnjn1efQsrn5LI579h/F4tUbWeBIVA/EJiTjp8VroC93o0ev7qF4/KGJdT5bY2W6hXTEc088BFtb+Y0MR06ew+/rtgnKiojI+pSf8bZ84Xh9oi1XuKnjLPZ17vV/foLog0dlsVFDByC8O0crJSIiIiKiyoUEt8LIAfJrXP/7eRlv1CQzkiTh/qF90abctaysnFwkpXLWEkuUlVeEr1YeQFyq+WCvrf1c8I9JIYjo4At1RdMq1jAPZ1s8PaItHhrQEvY2arP1a46mYf3xdBiNvB5KZO0MRiMW776MU+UKG7VqFZ4e3q7OZmuszN+zOL41oRO8XOTXZjNzi/DNyijkFZYIyo6IyLro9OaDnJS/ZlhfmF8L5QAvdS3q4DG89uEXsphPY0/0CO0oKCMiIiKyRCxupHrphccmwtHB3tQ2GAz49MffBGZElizm0DFZ28nRscJCNRIrPikV385bgeKSUlm8aaA/3n31efTtFQZJqv0LeBXx8vTAS08/hvGjh5nlEHPkFFau384LekRWTKfTY96KdWazePXp2Q0PTRwDtdr8Yr5IbVo2xwszHzGbVXZPzFGcOn9ZUFZERNZFVe7GsPr8Wa78zL4qiT8V1aWlq9bimzkLZLGmQQFY8P1nwr7fEBERERGR9Xjr2WmydnxSGhb/sVVQNmTJVCoVPNxc7hoj8XIKijF71QGkZBXI4jYaFSb3bY7nRwfD06VuB1yUJAk92zTGO5NCEBzkZrZ+8+kMrDuRXqc5EVHN23Q4Docupclidlo1nhvVDsFNzGdwFcXD2Q4vj+0A/0YOsnhKZj7m/HmAg08TEVVB+RncVSoVVKr6eY1Qo5Hf01N+kFuqXckpaZj05OvQlxtg94dP3jUbuJ2IFFBxMS1EZLX4EqZ6ycPNBU9OGyOLzf99A5JS+IM6ySUmpeDS1WuyWO8eXTlzo4XJyMrGt78uR1GxfJS9Nq2a48Wnp9fpbI2VUalUGNSvNx6bNsHsx57d+49g444oQZkRkVLrd+xDSpp8hNJuXTpiyrj7LLbooHnTIDz12FRoyhVeLlm9CYVFRZXsRUREfys/U6NOV39nMyx/Ian8BT6qPafPXsATs/4hi9nZ2WLVgu/g4e4mJikiIiIiIrIqfcI6oW/3TrLYpz8uNbtRlaigsAiHT56TxULat4KDfd0WydGdFRSX4ts1h5CeI/8d38PJFm9O6Iy+wT5QCbwu4eZki6dHtMOYHkFm67aezsT2s5kV7EVE1iAuLR9bjsbJYlqNCk+PbIeWfq6Csqqci4MNnr8vGN5u9rL41cRM7DzKwV6JiO6m/CDeBoPBbEDU+qJ8MSOvhdYdnU6HKU++hqQU+eAJb73wOMYMHyAoKyIiIrJULG6keuuVmVNhY3OrQK20VIcv5i4TmBFZoj1RMbK2o4MDuoZwuntLYjAYsWDFeuTmy0cnbd+mJZ6dMQ12traCMqtYt5COeGr6VLMCx4079+HytbhK9iIiS3UtNgHb9x6UxYIC/PDolHEWP2pdu9YtMemBUbJYTl4+Vq7fISgjIiLrYaOVFzeWlJbW2xtDyw8gUn7mX6odObl5GD/9ORQWym9W/O7TD9GlU7CgrIiIiIiIyBq99Yx89saLV+OwenOkoGzIUkUfPgndbQMcqVQq9O0eIi4hMmMwGDFn3VEkZeTL4t5u9nh5bAezAh5RJEnCkC4BmBLRHOXLLP84moajN/KE5EVE1acvM2DRrku4fcJDlQQ8MbQNWvpa7gy/zvZaPH9fe7g7yWd9Wh91DimZfC8iIroTbQUFfvV1sNfy51XRuVPtePvfXyFy/2FZbHBET/zrzecEZURERESWzLLvyCZSwN/HC4+MHyGL/bT4D2Rm5wrKiCxNUnIqLly+KouFd+8KGxtOd29Jdu8/jMvX42Wxls2a4MlHp1jsDJsd27fB9AfHy2Z0MxqBRSs3oLRUJzAzIroXOp0eC1duhNF460qeRq3GI5MfMBvFzlL17tEV7du0lMVijp7CqfMcsZSI6E483OUjURsMBiSnpgvKpnYlJCXL2h5ulnuzSn1hNBrx2PNv4OLla7L4zEen4LFpEwRlRURERERE1mp4/x4IaS//DfC/3/8m+12TGrbComIcOiGftbFT25Zw528AFmXnsWu4kpgli3m52GHW/cFwd7KswV4BoE97H0zt18IsvuxAMnKL6ueN8UT11eZjSUjMLJTFhoUGIDjIXVBGVefuZIvpg1rLiq11egMWbT4Gg4GfhYiIKmNrY37PXV5+fgVbWr/ccudlU8G5U81buW4LvvhhviwW6OeD37772GruuSIiIqK6ZSHFjcabVR+3L0pIkrJFVN9QuEgqBYvSx0xJ3urqL0bDHZc3nn5QNqtSfkEhvp3/+133q9KimFHBouTxFvj6sDCR0QdkbQd7e4SFdhKUDVUkNT0Lf27ZI4s18nDHMzOmWXwRareQjhgzcrAslpaZbXY+JBDfC+kuNu6MQkpahiw2cmh/+Pl6C8ro3kmShAcnjjGb5XbJ6k0oLilG5Z81rJWSz1dKF7p3Sr8DNUBKP8s3yL991Xt+OTk4mBX5xcYn1l3adaj8eQX5+8A6X5vW8zfgs2/mYM36rbJYt5COmP2f9+75WEJ/H2iQ7ylKWdrrhoiIiIjqA0mS8Naz8tkbj525hC17DgrKiCzN/iOnUKq7NfimJEno2yNEXEJkJjkzH+v3X5LF3Bxt8MLo9nBxsNxrouHtvDG2ZxNZrLDUgOUHU1hgTWQl4tILsPW4/Ddi/0YOGBYaICije9fC1wX9O/rKYteSMrHzKAd7JSKqjJOjAxzt7WSx2PgkQdnUrrhy5+Xj1UhQJg3HuYtXMOPFd2UxGxstfp/7Obw8PQRlRURERJZOIzoBotrUsmkAJt03AMvW7jDFvpm3Eq/OnAJHB3uBmZFoKalpOHdR/kNmr7BQiy+Ya0gMBiMWr9oAnU4+sufDk8bCvtyPK5ZqcL/eOHnmPK5ejzPFdu8/jC4d2qBls0CBmRHR3RQVl2B39BFZLCjAD0P69xGUUfV5uLli/P3D8dvvf5piOXn5iDlyGv3DuwrMjIjIsgX5+yAzO9fUjo1PRK+wLgIzqnlFRcVIS8+UxW4WN1Jt2RkZjbc/+lwW83B3w+/zv4WdneXNwkBERERENaw2BnuQFIx2r2Q2G6UDgSp5GFRKzrlMQcdQ9ngr+bfX33kWtPHDI9CqaQAuXY83xf77/W8Y3q+78r4FkqTqj9VsVLCvUDUyyO4tRUXFOHj8rCzWsW0LNHJ3rahzZZ0pyb0hFsL9dc4GgxGLt52EvuzW4ycBeGxwa3g4W/410f9n777Dm7iyPgD/Rl3uvTdMMWAwppree68JhJLeSNm03WyS3c3uZlO+Tdn0ThJKEiAEQgu9GbDBNFOMjTHGvfeqru8PE5kruYAleST5vM/jB+bMjOZIlmRp7px7JscGI7ukDhcymyeLvJhbh3PZdRjSrYXnmUbZ8YPVV7a/jbUozehkJDbz2hSJU8f3FZl5/YPIjPNVAjPeh7Xq9rdpizm/L3Pej2SuHd8XAPRmXMYnuvsuVHq9HhtPZDMfCQUCDisn9IRIaF9/R+cMC8OV7EqU1igMsV0nUjEkKgTuLi2/l3Iw43Oh2X+2zHh8zflcp+fzMyGPxzbndW122ubcAF/7WmJ/fnB8fu+xs+9cHMchLCQAqdezDLGcvHwMGhDNX1JWkpNvPNGrmROam/XdhcfvPWZ9X7vzfWvr6rH4oedQV892hf7ojZcxbGA/3PVjYM53e72Z54DMYVbelj0vQAghhNgL+zoTQUgH/HX1Sma5vLIa3/y8k6dsiK2IT2Bnq5XLZBg2eABP2ZCWHDt1Dhm3DcIDwLiRw9CrRzeeMrp7AoEAK+9dALGoeRBCrwfWbzEt2iSE2JakCylQqlSGZY7jsOreBRAKzbh4iUcjhw1C756RTCz+9HmaOZkQQtpgXOTniJ0bjQfzhEIBAv19eMrG8eXlF2LpI3+CTnfbxYoch5+++R/CQ4N5zIwQQgghhBBi74RCIf7yxDImFp90ESfPXuYpI2IrTiebnuseG+dYkzfZu8PJWcgqqmZi42MC0T3QjaeM7t49YyLhImOLsjYnFaOmkcZDCbFlWSX1yC6tZ2LTBgYjxMeZp4w6TiIWYvmEHkw5llqrQ8LlbN5yIoQQWxcW5PhjoSqVCoVFJUwsLMjM4kbSKr1ej0ee/wdS0zOZ+P33zMFjKxfxlBUhXQPHcfRz64cQYr+ouJE4vJg+PTBr4kgm9v7XP0OpVLWyB3F0JaVluHrtOhMLDw1GaVkFKquqoVLRc4NvGo0W+44kMjFvL0/MmzWFp4w6zt/XB3NnTGJipRVVOH3hCk8ZEULao9frEX/6PBOLie6NoED7PcHJcRymThzDxIpKynH9Zg5PGRFCiO0zHtjKKyiCVsvj7I5WkJPLDlIG+fsyE3MQy1GpVFjy4DMmnTL//cpzmDphTCt7EUIIIYQQQsidW7lgKkICfZnYO1/8yFM2xBYoFEqcOp/CxKJ7RcLHy4OfhIiJitpG7Epkx6193WWYMzSMp4w6xlUuxr1j2AkWG1RabDtXzFNGhJA7cfwqW+zh5SLFtEEhPGVjvh6BbhgWxX4WOnEpC1otdT8ihJCWGHcwzMkrdLgJsvMKipj7xHEcQgL9eMzIsX341Xr8smM/E4vt1xufv/MaFRwRQgghpF10xRrpEl55aiV2H04wLOcXlWLDtv14eNVSHrMifEk8Y9qpKu36DaRdv2FYFovEcHFxgrOTE5yd5HB2coKLi7Ph/87OTnBxdoKPt5ddf/HS6/W4cPEKziZfhrOTHLOmToSHhzvfaSE55Rpq6tgZAlfcMw8yqZSnjMwzYcwInL90FTezcw2x+FPnMWroALt+/hDiqDKyclFYXMbExo4cylM2lhPVIxL+vj4oLm2+b/GnLqBXZDiPWRFCiO0y7tyoVqtxKSUNA2OiecrIsvR6Pc5cuMTEwo3uM7GcF/72Fk6dvcDEZk+biFdfWM1TRoQQQgghhBBHI5GI8cIj9+CFNz4zxHYfPoWLVzMwYKhvG3sSR5WUfBUKpZKJNTQqcPB40q0xTxmc5XI4O8nh4iyHk0wCgcBx58fW6XSoqKqBp7srhEIh3+kAAI5fyoHmtqIbDsCK8T0gEdtGfndjYHcfDLxRjguZ5YbYuawazBvkBw8nMY+ZEUJaUqdQ43wmOxHbmGh/iIT2/XdgfL9AnL5WaliurlfgcmYRYnsG8ZgVIYTYJuOx0IbGRhSVlCHQ33G+P2Zm5TLLAb5ekEros6k1xCeexV/+/QET83B3xZY1H0Aul/GUFSGEEELsCRU3ki5h5JD+GBsXi/jTyYbYf7/cgAeWL7GZgQvSeYqKS9vdRq1Ro7KqGpVV1W1u5+3liZX3LoCHO/8FgXervr4B23/fj/SMm4bYzr2HsHLpQh6zahJ/iu2Y1iMyHFE9IlvZ2vYJBALMmDwOn6/ZYIjlFZYgK7cA3cKCecyMENKS+FNs4YGfj7ddvwf9geM4jBk5FFu27zHEklPSUVVTCw83Vx4zI4QQ2+Ti7ITIsGBk5uQbYvEJSQ5T3JiVk4e8giIm1r9PT56ycWwbNv+Gz75dz8QiI8Kw/sv3IRAIHG4WXEIIIYQQQgh/Hl06G29+uh7llTWG2P99+RN+GjqCx6wIX85dTjOJZebkM+c6bscBkMukTcWOfxQ/Osnh7uqCPj3C4enhZuWMrSczpwDbD5xEdW0dPNxc8ODiGXB3c+E1J7VGh8SUPCY2sq8/ugfa7+N8z5hIpORUQqVpKtjU6YGE61WYOcBxLpAnxFEkXiuDRtd8XlIk4DC8t38be9iHUF8XRPi7IKu4zhA7fvEmFTcSQkgLvDzc4ObihJq6BkMsIekcFs2ZzmNWlqPX63Ey6RwTCw+hiV6tobC4FEsf+zO0Wi0TX//ZO4iMCAV02lb2JIQQQghpZt/TLRFyF155aiWzfP1mHn7dfZCnbAifekRGWOy2yisqsWX7Huh0uvY3tiE3bmbji+/WM4WNAFBc2n7hp7XlF5UgI4sdyBs7chhP2VhO36ge8PbyZGLHjIo4CSH8UyiVSE65xsTGjhzqMLNVDx8SC7G4eRY6nU6Hsxev8pgRIYTYtrHDBzLL1zJuovAOJkuxB8cSkphlb093RPfqxlM2jutSShoee/41JiaTSbF13efwcLffixUJIYQQQgghtsnZSY5nH1jMxDbvPoqMzGyeMiJ80ev1aFQo29/QaJ+GRgVKyytxM7cAV65l4vSFFOyPP42vfvwNZRVV1knWirRaLQ4cP4t1W/ehurap0KWqpg4Xrl7nOTMgOaMIdQo1E5vQP5CnbCzDVS7G0F5sIePJ61XQ6mhiJ0JsiU6vx4lU9jz3oB4+cJU7RiersdHse+m1nDIUVdTylA0hhNgujuMwdEBfJpaYdAEqtbqVPexL+o2bKC4pY2JxsY4xia0tUavVuOeRF1Fk9Fj//cUnMGvKWJ6yIoQQQog9coyrtAm5A1PHDsPA6F5M7O2P11CHhC5o/OjhWDh7OgbGRKNX90gEBfjD3c0VIlHHmtnmFRQiPuG0hbO0Do1Gg/2H47F+41bU3Tbr0h/EIv5PVht3THNzdUFsvz48ZWM5AoEAY0YMYWLnL6Whrt7090AI4U9OfjG02uaCdYFAgOFDBraxh31xkssxeEA/JnYzp4CnbAghxPYN7BcFFyc5EzueeIanbCynrr4e55OvMLExcQMdppjfVlRV12DhqifR2Khg4l998B8McIDvOIQQQgghhBDb9PT9C+Di3PxdVqfT4b+ffcdjRoQPHMdh/IhB4DjOIrenUKrw6+9HTTpx2LLyymp8u2k3Tp67bLKuvkHRwh6d6/jlHGa5V7A7AjydeMrGcsb0ZbvhVDdqcCmXiooIsSUFFY0or2UL4MdEO04nq4GR3nCRsdf/XM4o4ikbQgixbWOGxTLLDY2NOGc0hmiv4hPYMV1/Hy/0igzlKRvH9Zd/f4CTSez1ntMmjMI/XnyCp4wIIYQQYq86VslDiB3iOA6vPLUS96z+uyF2MSUdew+fxIxJo3nMjHQ2oVCImH59EGN0Maler4dKpUJ9QyPq6upR39CI+oaGpp/6RtTV1xv+X11TC7WmeZaiYydPo3tEOEJDgjr77tyxsvIK/LpjDwqLSlrdRizm98+CWqPBmeQUJjYqbnCHC09tzchhg7Br3xFoNBoAgEarRdKFFEwcPZTnzAghf8jJL2SWgwP94WRU1GLvekSG49TZ5hOLOfk0mEcIIa0Ri0QYOXQA9h87ZYidOnsBc6ZPhFwm4zEz85w4dQ6a2y5GFImEGDm4P48ZOR6tVotVT76EGzfZCxWffGg5Vi1dyFNWhBBCCCGEkK7A090VTyyfh/e+3miIrdu0Ha+/tBrBgf48ZkY628ghMYjuFYmi0vJb454K1DU0oL5BcWu5EfWNCjQ0Ku5oQuDCkjIcPnkOU8YO64TsO06v1+NCynXsOXoa6ltjcsYkYn4nfM0rqUZmYRUTG9PXMV6fIT7OiPR3RWZxc0Hj8WuVGBjuxmNWhJDb5ZTWM8verlJE+LnwlI3liUUCxEZ648TVYkMsp6SKv4QIIcSG+fl4ok+PCKRmZBli8QlJGDHUvicBr6quwcUrqUxsbFwsOI6jZigWtHHbHnz09QYmFh4ahA2fvwOhUMhTVoR0TRzX9NPV0WNAiH1zjGoRQu7Qgulj0SsyFOmZuYbY2x+voeJGAqCpAFYqlUIqlcLL06PNbfMLirBmwybodE3dvfR6Pbbu2osnHlwOqVTaCdneOb1ejwsXr2DPwaNQq1sewPuDRMLvQF5BUSkUShUTGzV8SCtb2x8XZ2cMjOmLM+cvGWI3svOouJEQG5KTX8wsh9lw0XpHGd+n8spq1NU3MrO5E0IIaTZmWCwOxJ/CH+NcCoUS23btx32L5/KbWAeVV1Ri36F4Jjaof2+4ONt/ZwBbodFocP/qP2Pn3kNMfNigAfjfm6/xlBUhhBBCCCGkK3n+4SX45IdfoVQ1TdSpUqnxwRdr8f6//8JzZqSzubu5wN2t7YIVnU6HhkYF6usbDAWPdfVNRZBXr99ERVWNYduEc5fRs1soIkIDrZ16hzQqlNh5MAFXb7s4uyUSnid8PZ+ezyy7OYkRE+HFUzaWNyY6gCluTC9uQG2jBq5yukTJHqm0euRXq5FTqUZulQa5VWpUK3RQa/VQa5tOmooEHCRCwEUqRKiHCKEeYoR5SRDqKYZUJOD5HhBjOWVscWO4n4vFOv3ainA/F7a4sbiKv2QIIcTGjY2LZYobs3PzkZ5xE716dOMvKTMdPp5ouK4TaPr8HzewL48ZOZ6k85fx6AuvMzGpVIItaz6At5cHP0kRQgghxK7RmUPSpQiFQrz85Ao8/Oe3DbETSRdw/NR5jBk+iMfMiL0JDgrA+FHDcfh4giFWWVWN3w8cwYLZ03nMjNXYqMDOvQdxNe26yTqhUICgAH/k3talTCKWdGZ6Joy7h/n5eMPLw52nbKyjV/duTHEjXx3T9Ho9GhsVUCiV8HB3g0BAg0odUVtXj5z8IuTkF6OwpAwKlQpqtQYajRYioRBisQgSiRj+Pl4ICw5AeHAA3N0cb3DIkRi/Jh2xuDHQ3xcikcjQRRYAcguK0Ken/Z6YJoQQa/L2dEdMn564eLX5M/WJU2cxKCYavXt15zGzu6fT6bBh829QqtgJRcaPGMxTRo5Ho9FgxeMvYNO23Uzcx9sLW3741OYmwyGEEEIIIYQ4pkA/bzywZAa++nGHIfb1us149bnH6CJDYkIgEMDF2QkuTjKTdf2iIvHNzzug1WoB3Jrwde9RPLlyIeQy2/qOm51XhK37jqO6tq7dbcU8FzcaF9kM7ekLodBxxupiu3vj5/gbUGmaLyjPqVAgOthxOsM5urJ6HU7kqJFa1ojCGg107TQ4Umn1UGmBOpUGRbUanMlVAAA4AH6uIkT5STE60glB7vxOtkyaGHduDPN1vNdmqA97n8qrG1DfqIKznN9rYgghxBb1790dXh5uzKQmP27ZjtdeWA2JxP7eN7Nz83E4PpGJDYvtCye56fcd0jGJZ5IxY9mTqG9oZOKfvv0aBg+I5ikrQgghhNg7Km4kXc7y+VPx+gdrkFdYYoi988kaKm4kd230iKHIuJmNnLzmmTUvXklFz8hu6Nc3isfMmmRl52Lrzr2oaWEAz9fHC4vmzkB6xk2muJH3gTzjoqJQxysqCg8NZpbLK6tR39AIZyfLd0xTq9WorKpGVXUN8+8f/1fe6pLp5uqCB5YvabdjKQEqq2twJvkqMrPzkVNQhKrq2vZ3MuLm4ozQIH9EhAZiyIC+8PdxnJl47V2jQomSsgom5ojFjUKhECGB/sjKbf77lZNPxY2EENKWRTMnIi0jy9DxAgA2bP4Nr730FOQy+xkIO3HqLK5l3GRio4YOQESIbXZbsDdqtRr3Pfo8tuzYw8SlUgk2f/cxQh3wcwUhhBBCCCHEdv35saX45uddhm4V9Q2N+OTbH/HPvzzFc2bEngT4eWPSqCHYH3/aEKuprceugyexeNYEm5jMUavV4tjpizh+5hL0etMKrCH9o5BfXIbCknJDjM/OjXq93qS4McLPsQqLxEIBQnyckVnUPI6WW955xY16vR61jSpU1SlQWadAdb0SzjIJ+kf4QiIWdkoO9kin1yOlRIvjWSqklmrRTj3jHdEDKK7VoLhWg/gb9ejuI8GY7s6IDZZBJOD//aMr0mh1KKhgCxFCHbC4MdBTDpGQg0bb/EzOLalC73A/HrMihBDbJBAIMGn0UPyy65AhVlpWge17DmLJvJk8Znb31BoN1m3cxnRtFAgEmDCSJnq1lIQzyZix9AnU1rGTJTx030I8vHwhT1kRQgghxBFQcSPpciQSMV56fBme++dHhtiewyeRfCUNsf1685gZsTcCgQAL50zDF2s2MJ1Pdu07hNDgQLi7u/GSl1arxdETiTiReLbFAbyhg2IwdeJYiMViXElNZ9ZJxPzOlGhS3BjseBf/ttwxrRi9e0Tc9W1ptVrU1NY1FStWVaOyuhpVVTWorG4qYKyvb2z/RgDU1NYh6Vwypk8ef9c5dAU6nR7XbmQh/tR5XErNaPF1dTdq6uqRkp6JlPRM7D50Er17hGNs3CD0793DoWbltUdFt13YADS9zwcF+vOUjXWFhgQxxY0FxWU8ZkMIIbbPx8sD86ePx6YdBwyxiqpqbNu1H/ctnstjZneuvKIS23btZ2JeHm5YOGMCTxk5FpVKhaWP/MnkMZbJpNi+4StMGDOCp8wIIYQQQgghXVVkWBCWzZmIH7cfNMQ++XYDXlz9AFxdnHnMjNibEYP7ISMrD5k5zeeUU9Iz0SsyFAP69uQxM6Cyuha//n4YeUWlJuvkMinmTRmF3t3D8eWP25l1fI6Jltc0oEGhZmKO2DUtzMeFKW7Mqbizccs7odfroVBpUFmnQFW9EpW1ClTVNxUyVt0qZtRodSb7Xc0pw8pJ/SyWh6NQafU4dlOF49lqVDRaoqSxdTfKVLhRpoKrVIBR3ZwwsZcLnCQ0PtqZCisboTFqxRnq43ifC4RCAYK9nZFd0jwZeG5xNRU3EkJIK8bFxeLspVTczCkwxI6eOI2B/fuiR2QEf4ndpd8PHEVhcQkTmzlhOAL9vHnKyLGcOH0eM5c9ibr6BiY+bcIofPr2qzxlRQghhBBHYSPFjRxgAzP62T9zHkMzH3+zfn9mnKjkOjar3iPL5uM/H69FWUWVIfbOJ99h41f/vePb0GtV7W/UBk4oNWNnMx5vzswTw3rTQYDOO7Z1T6R3hIe7O2ZNnYitu/YaYgqlEtt278OqpYsgEHTuifiKyir8umMP8guKTNY5Ockxd8Zk9O7VwxBTq9mBM4lEYvUcW6PWaFBQzA48dqWOaS0VN+r1etTXNzDdFm//t6a2jplpyhwKhdIit3PXbPjvf6NCgYSzl3D81AWUlFda7ThpGdlIy8iGh5sLRg+LxeihsXBzdbwBJHugULKvAxdnJ4hFNvJx2cI83F2Z5duL9Akh7bDhv13WZaf324K/rzFxg3DhyjWkZ+YYYidOnUXPyAgMHRRjseNYg0KpxJoNv5i83y9fOANyuYU7T5r1tc2MnXn8vqhUKnHvQ89i+56DTFwul2H7hq8wefyodibIsL3vuoQQQgghpKuw1/EmM/I2d9iGr3GyDt7nl1cvZ4obK6tq8PXaTXjxyfvv+Db0Ok37G7WBE5pRRGbW79rc4jUzxk06OIbdtK+Z3xF12o7v28rzmwMwf+pofLFhGxpvG0/afegkwoJ84WmRCV/v/rV1KfUGdh9OhFJpen67W2gAFkwd0zTeotdDpVIzX7/FImHzuQRzzil04D0ht4gdc3KSiuDlasa1AzYq1Jcd68qtUAIi6R0/ZmqNFlW1jaisbUBVXdO/leUVqKpXoKq2EUp1G891TtTi1VCZBeVQKxVNv//OJDDzeGJzri1p+29PZrkKG85Vo6TOjPeODqhV6rA3rQ6J2Y24b5gfooONxkbNfczM+Ztrzt96c89RmvM3V+h0R5tVNLC/ay8XKZykjjkealzcWFZVDaibizH0fF3rBgBmfD7izHl96M18bZlzrZw5n48AoLPfuw34vKbSDo/bdHAej03u2q3nikAoxKpFM/DWJ2uhvjVhv16vx/rNv+HV51dDKuXvmsI7lZ2bjwNHTjCx0EA/TBsXx1NGLTDrswKf3xX1OJZwFrNXPIX6BnbSkpmTxmDLmg8gk0ps8vpes5jzXmq3j4W95k04jgPXZa9nakaPASH2zTHPThDSDie5DM8+dA/+8d7XhtgvOw/g33/JRq/u4TxmRuxRTL8+uJ6ZhctX0wyxrJw8JJw+h9EjhnZKDnq9HpeupGL3/sNNg3NGIiPCsGD2NLi6srN+Gm8rFvP3Z6G4tBxaoxk0Q4MDecrGum7vmKbRaJCSloHQAG+TAsaq6hqo1eZdNHCnPD3cO+U49iI55Rp+/m0fausa2t1WIhYjJDgAYSFBcHdzhVgkaurOqdVCo9agtq4eOfkFyM0vbLOItKqmDrsOnsDB40lYOHMCRg0ZQF+2Oplaw77exDx3s7UmsYi9b531XkMIIfZMIOCwYtFMvPnRGihv+xy9duNWyGRS9O8bxWN2rVNrNPjqh5+RlZPHxEcNHYA+PbvxlJXjUCqVWPzAU9i17wgTd3KSY+dPX1PHRkIIIYQQQgiv+kVFYu7kUdhx8KQh9sGX6/D0Q8vs4uJUYjvcXJ0xZ9IobN592BBTqdXYuiceD94zs1MnfFUqVdh95BQupd4wWScQcJg0chBGDu7HjLGojM6BS3gcE80pqWaWw3ydHXI8yLgbZWW9GrUKDVxv1abodDpU1ykMhYtVdY2orGn6f3VdI+oaWxhT05g3UaOA4yDs5MmJbZVKq8eulFoczmh/LNSaqhu1+OJYIeIiXbFokA+cJHwVL3UdKjV7TYZc6riPudzo+aTWWGbiaEIIcVT+vt6YM2U0tu45aoiVllVgzYbNePyBZRAKbfdvRnlFJb764WemSYBAIMDKxTNsOm97ceREEuasfBoNjQomPnvKWPzy7Qd0foEQQgghFkHFjaTLemrVIrz75Y+orasH0FQc9t/Pf8C377/Oc2bEHs2aOgE5efmorqk1xA4fT0BkRBiCggKsemyFQoHd+w7j8tVrJusEAgEmjRuJkXFDWhwUU2uMOjfyWMjTaFT05SSXW76LjI3w9vRAfX09cnJyUFdXh7ycLFRXlLa/Ywc5O8vh6eEOT3d3eHi4wdPdHQePnUTDbTMpeXt5Wu349qSuvhGbdx7A2YtXW91GKpVgSGx/dO8WjrCQIAT4+dzRoL1Op0NZeSVy8gqQmZ2LM+cvob7BdMBQoVThp237cOHKNSxfMANeHpaY8ZjcCY2GnaVM5MAnOEVGHSk1GipuJISQO+Hj5YEFMydi42/7DDGdTodv1m3Co6vutbkCR5VajW/XbcK165lM3MvDDQtnTuQpK8ehUCixcNUT2HPwGBN3dnbCrp+/wbhRNjQTLCGEEEIIIaTL+uvq5UxxY2FxKdZu3oHHVi7mMStij/r26oaBWb1wISXdEMstLEb86WSMHzGoU3LIKyzBr3viUVlda7LOy8MVi6aPRXCAr8k64wn+xCL+LpUpq6pnlkN8nFvZ0r75e8ghFgqgVGtQXV2NxsZG/LRPARmUqKptQE29ArpO7mbi6SKFQOB4haR3K7NchfVnq1Ba336hl0DAIcjTCaG+zgjzdUGYrzO83WSQCAUQiQTQ6/RQa/VoVGmQX16P3NJ65JTVIbe0HlX1d16MejqzFmmFDVg2zA/9jLs4EotSG004LRI6bsGv2Oi+aai4kRBC2jVx1BBcSEnHzZwCQ+xKajrWbtyKB5Yt6tRJTe5UdU0tPv5qLaqqa5j4zAnDEdLCdwNydw7Fn8LcVU+j0aiwcd70Cdj09XuQSBx34nhCCCGEdC4qbiRdlqeHG564fwne/ewHQ2z9LzvxzxefQEiQP3+JEbskk8mwcPZ0/PDzFuhvDcLodDps3bkXjz14HyQS68xOk5tXgF937DH5cg4A3l4eWDR3JoICW38+m3Zu5O/LpsmgIo8zplqTXq/HtfTrSE1NZZ4r5pBKJfBwd2sqYPRwN/zfw90NHu5uJs8/rVaLnXsPMTEvTw+zcnAE7XVrDPT3w9iRwzBscAzksrsvvBUIBPDz9YafrzeGDOyPBbOm4PylFMQnJOFmdp7J9qnXs/Cfj9ZQF8dOJDQa4NLqtK1saf+0WvY9l2aqI4SQOzdmWCyKS8pxJOGsIaa51R3x/mULMXRgDI/ZNWtUKPDFdz8iIzObiTs7yfHUA/dALpPylJljaGxUYMHKJ7DvcDwTd3F2xu5N32LMiKE8ZUYIIYQQQgghrOGDojFh1FAcOXnGEHv3s+/x0LL5JpOgEdKeGeOHIzu/CBVVzWOT8UkX0T08GKFWHGPX6XQ4ceYyjp66AJ3OtCBuYHQPzBgX1+KFtXq93rRzI48X4KqMJlp0ljrm61Ag4KBWNSLlajoUiqYLoa/IGuDjbLkL4jkOcHeWwd1ZBk9XOTxcZPB0ufWvqxxnr+Uj/lKWYXtPV7nFjm2vTmQ2YGOy6bUFt+M4oH+4F0b39UfPIHeIRW38zoQchEJAJhHC00WKfuFehlXV9SqczSjF8ZRilNUoWr+NP7Zv1OLLY4WY0c8TMwf40tiolRgXFQsc+HE2LmbWdnJBNSGE2COBQIBVi2fiv5+vZxoVnL1wGXq9HvcvXWhT3yPLK6rwyddrUVpewcQjw4IwbRxNQmqu/UdPYv79z0Jh1LRiwcxJ+PnL/1JhIyGEEEIsynY+ZRLCg+cfW4GPv/0JSmXTjHFqtQbvf7kO//v3n3nOjNij8LAQjB4+FMcTkwyxsooK7D98HLOnT7LosXQ6HY4nJOHYydMtFsYNjInGjCnj2y2qNC5u5PMLp0nHNBs6EWIpNTW12LZjNy5cvGQobAQAbTvFjUKh4FahovutAsbm/3u4u0Eul93V4E5VdQ1zfADw8nS/uzvjQLRaHTZu34eTZy62uD66d09MnTAGPSLDLTqIJhaLETc4FnGDY5GTV4BDxxJw5sIlZps/ujheTr2Bh5bOgdRKhdKkiVjEvgcaF107ErWmaxSUE0KINXAch0WzJqFRqcSpc5cNcZ1Oh+9/3ILMrFzMnzkFUil/f7czbmZj/aZtKC1jB/JkUgmefvAeBPr78JSZY2hoaMT8FY/jwNETTNzVxRl7fvkeI4d1TrcKQgghhBBCCLlTf332Eaa48UZWLn7ZuR/LFszkMStijyQSMRZOH4fvNu82jFHqdDps3XsMTyyfb5XzIdU1ddi6Nx7Z+cUm62RSCWZPGI5+Ud1a3V+r1ZmMi0l4PCeuNhoTFYscb/JBnU6HhOTrSEm5CoWyeTxC20JhanucZVJ4uMrh4SqHpxRMAaO7s8xk4srbVdY1MsteLnc/eakj2ZNai92p9a2ud5WLMbKPP0b18YeXq/kTo7k7SzBpQDAmxAQhLa8Kx1OKcCW7Eu3Vl+25UolGtR4Lh/g5dOEdX4w7NWq0jtvN0LhLpXEnR0IIIS3z9/HC6vsX45PvNkOlbr628FzyFSgUSjy0fAnkcv4/V+UXFuOzb9ebNIUI8vfBkysX0CTfZtp7+AQWPPCs4drqPyyePQU/fvEOr000CCGEEOKY6Cpm0qUF+PngwaXz8OXaXwyxbzb8itf+9Ah8vD15zIzYq/Gjh+PGzWwUFDUPrp29cAnJl1MgEokgEgkhEoogFApuLYsgEgohFAqb1rW0LBJCKGj+v0gkwqUrqcjJKzA5vkwmxZzpkxHdp9cd5Wtc3CLh8UunQMCeSDa3m6GtuZp6DTt/34dGhcLkvgkEAri5ujBdF2//v6uri0WL6ioqq5hlJyc5ZB3oROgI1BoNvvt5Oy5evW6yTi6X4Z75MzFskPW7JoaFBOHB5YsxdFAMftqyw+TE2+W0DHz6/WY8uWoxnGzgBKGjkhoVeDc0NEKn05m8PzmCunq2Qymf7/+EEGKPBAIOyxfMAPR6nDp/hVl37ORppKSlY8U989Gre+sX1lmDSqXCjj2HcOTEKZOL9uQyKVY/sAThIYGdmpOjqa9vwNzlj+JwfCITd3N1wd5fvsfwoQN5yowQQgghhBBCWjd57HAMGRCNsxdTDLF3Pl6DpfNnUGcsctdCAv0wLi4WRxLPG2KV1bXYfuA4+kd1vzUe2jTWKRQ2/79pDFRgWBYIBO0+/65ez8KOAyehMLqgFgDCgv2wcNpYeLg6t3kbt1+Q/Qcxj5OsarXGXdN4SsRKqmsbsO3wWWQXlJmsa6moTSoWwd1VDk9XJ3i4yG91YJTD061pmSlEVdTeVS4VNUbFja5dd4xt+5VaHEhvubBRLBRg9rAwjOsXYFL4ZgkCjkPfUE/0DfVEWY0CG+MzkZZX1eY+R69VQaXVY1mcP/2dsjCJ0e/YuADQkag1RsWNbXUhJYQQwugeHozHVyzAF+t+hUbbPDlHStp1vPHep1hxzzz0jerJS25arRYHjp7E7/uPMLkBgK+3B555cAmcnahjtzl2HziGRQ89Z9I4495507Hu0zepsJEQQgghVkHFjaTL+/PqB/DNhq3Q3vqi09CowMdrfsa//7Ka58yIPRIKhVg0dwa+/O5HqDXNX+40Gq1JZ0JLCw8NxsI50+Hu7nbH+6hU7EAgn188xWJ2tiRH6ZimUqmw98BhnE9u7sh3e3FjYGAgJo0diT89el+n5WRc3NhVuzaq1Rp8sW4L0jKyTNb17xuF+xbPhbuba6fm1K9PL/ztpafw6469SDxzgVl3IzsfH377M/708FI6CWclfj5ezLJao0FRSRmCAvx4ysh6cvMKmWV/o/tOCCGkfUKhACsWzYJMJsXRhHPMurLySnz4xfcYNyqu07o4ttatEQBcXZzwzEP3IiTQ3+p5OLL6+gbMXvYIjp44xcTd3Vyxb8sPGDZ4AE+ZEUIIIYQQQkjbOI7DX599GIsffsEQu5x6HbsPxGP21HE8Zkbs1ZhhA3AjOx85Bc0Tvl69noWr17Pu+DY4joNQILhV+CgwKX7U6/UoKC432U8g4DAuLhZjhsU0TU7YzoSpqhbGHPns3CgSGXdNu/tuhrYqJSMPu45dgOLWRdC626oZ5XI5+vcMRUyoa1MB462CRrlUbLXitcpaKm4Emjo2tlbYGBngihXje8DPo3PGHn3cZHhqVh8kppVga0IWFOrWr6FIyKiGXCzA/EG+VOBoQXIp+/5XUauETqeHwNEqrQGU1yqYZbmUCjEIIeRu9OkZgdX3L8JXG7ZBeVuRW1V1DT79Zj1GDhuERXOmd2oXx4KiEqzftA3Zufkm6wL9vPHsg0vg3s7kJ6RtO/cdxeKHnzO5dvO+hTPxw8f/gYjHiWIIIW3gbv10dfQYEGLX6FMG6fK6hQVj2fzp2PDrbkPs0+9+xp9X3w9XF/qiQ+6et5cnpk8eh517D3bK8QQCAcaNisOYkcPuuruY8ew6Egl/J3ONu4YplEq775iWX1CIrdt3obyikomr1RpIJBJ069YNbm5ukMmknZqXcT5eHh6denxboNVqsebn30wKG8UiEZYtnou4wdbv1tgaJ7kcK+9dgIEx0Vj786+ob2geeM0rLMFna3/Bsw8thawTiiS6GlcXJ3i6u6Hyts6ZOXkFDlfcqNPpkJvPFjeGBQfwlA0hhNg3gYDDktmT4evtid/2HjUZ5Dl28jTOJV/GyGGDMHr4UPh4e1r0+FqtFpdT0xF/8jTSrme2uE230CA8cO8c+Fr42F1NbW0dZi19GMcTzzBxD3c37P91LYYM7M9TZoQQQgghhBByZ+bPmIjePbsh7fpNQ+ztj7/FrCljqWiE3DWBQIAF08fiyw3boVSZdlW8E3q9HhqttqnbSkst/Vrg4eaCRTPGITTozs/btzShqpjH4kax0GjCVwfomqZUqbH35CUkp2UzcZ2u6fcaGBiIoKAgTIrriSjfznnsG5VqNCjZsXAvl65X3Hgisx67U00LG8VCAeYMC8P4/oGdXtTGcRxG9vFH7xAP/HTsRptdHA+lVsJNLsKkvjRJp6WE+DgxyyqNDsVVjQj0cmplD/uVW8o+90N8OndiY0IIcQS9e0Tg2YfuxWdrt6ChkS0aT0g6j6vXMnDP/JmIie5t1Wv8FAoljpw4hT0Hjpp0awSAiJAArL5/EVxosniz/Pb7Idz72Ism36FWLJ6N7z96A0Kj7zKEEEIIIZZkvxUjhFjQy888yCxXVdfiq/VbeMqGOIJBA/ohbnCs1Y/j4e6GB5cvwbjRwzt0gkCtYb+IGhcYdiYfLw9mWXOrY5o90ul0OH4yEWvW/mhSSAgAzi4uiI6OhptbU5dNS1/k3h7jzo3eXl3rQne9Xo/1v/6OS6kZTFwmleLpR1dh+JBYm7iQo1+fXnj+yYdMukdm5Rbiqw1brd4NtqsKC2GL/HLyTGd7s3elZRVQKJVMzPh+E0IIuXMcx2HCyCF47dmH0D08xGR9XX0D9h85gdff+RCfr9mAK6npTCfvjqiuqcWeg0fx97f+h69/+LnFwkaRSIgFMybgxSdWUGGjmWpqajHjngdNChs9Pdxx6LcNVNhICCGEEEIIsQsCgQAvP/0QE0s8exHxied4yojYO093N8yaOKLTjte/dySeWD73rgobAUClZgvcRCIhr5Oryoy6plXVKVvZ0j7kF1fg618OmxQ2arU6cEIRevfujZCQEAgEAsgknXcxdEUN27VRwHHwcO7cCWf5VlitwqbkWpO4h7MEf1kUg4kDgnjt1uflKsVTs/pg9tCwNrfbfqEUeZWKNrchd85VLoGHMzuJbm5ZHU/ZWE9NgwpV9WzxfZgfFTcSQkhHdAsLwitP34/ePcJN1lVV1+DrtRvx+jsfYf+R46irb7lbdEcVFBZj49ZdeOWNd7Fz7yGTwkaO4zB5zFA8/8hSKmw006+7DuCeR00LG++/dx4VNhJCCCGkU1DnRkIAREf1wLxp47F931FD7IMv1+PpB5d2ekc14hg4jsOMKRMwbtRw1NbXQ6PRQntr5lGNRgONRgOtVtf0/1sx42XDPi1sAwDdwkMxYtggyGQdn2FSZTSTqpjH4kZXF2d4uLuiqrp5gMUeO6ZVV9dg247dyMrJNVknkYgxc+pk/Lztd9TWNZ/MCQvq3KKiispqZtnL06NTj8+3hLOXkHQhhYnJZTI8+/j9CA8N5imrlgUF+uOF1Q/jf198h6rbugleu5GNvUcTMHvyGB6zc0xhwQG4mJJuWM7JK+AxG+swvk9urs7wcKPBPEIIMZefjxeef+w+HE04h+37j5kM/Oj1elxJTceV1HQ4OzkhLCQQYSFBCAsNRlhwELw83VucYEGlViO/oAg5eQWGn8Li0jYLJLuFBmHl4lkI8PO2+P3saqprajBjyUNIPHOeiXt7eeLgtvWI7d8Xer39d1gghBBCCCGEdA33LZyJ1//7OXLyCw2xdz75FuNGDuExK2LPYvr0gFarQ/LV62hUKJvGMrVaaDRN46J/jI+aQyIRYdaEEYjp071Dk1Oq1MaTvfJ7mUyQN3s+PqfMsheAdxadTocTF9Jx7EwqdC103vT390Ff+EEkanq8OQ4I9JAB+s4p5qyobWCWPVxkEAq7zvzvWp0OHx4phfFvxtdNhqdn94W3m210seQ4DtMHh8BJKsTmEzdb3EanBzYkFOHPM8Ih5LEY05GE+bqgqr7CsJxTUo9hvXhMyAqMuzZKxEL4eTjzlA0hhNg/b093PPPgPTiRdBFb9xyBUsVOIFJeUYnfdh/Arn1HMGhANIYPiUV4aDDkHbiusaq6BtdvZOH4qTPIyMxudTt/Hy+sWjQd3cKC7voYhPXLjn2474m/QGv03e3BZQvwzQf/An0EI4QQQkhnoOJGQm7567MPM8WNRSVlWLt5Jx5ftZi/pIjdc3KSw8nZie80WqTT6Uw6v0kk/BU3Ak1FRcbFjcOHxPKX0F1KuZqGnb/vM+mIBgAhwYFYOHc2BEIhU9gINN3vzqLVapkiOaBrFTdWVFXj192HmJhYLMbqh5fbXGHjH3x9vPDMY/fjf5+vQV1980Ds3qOnMKBvL4QG+fOYneMxLjbOyy+CSq3mtbOtpd00Kr7u7AJrQghxZAKBABNHD0W/3t2xeccBXL3e8gU59Q0NSE2/gdT0G4aYXC6DXCaDWCyCUCCAWqOBWq1BTW3dHXd6dJbLMH3CCEwYNZTXDgSOoqq6BtMW3Y+k8xeZuI+3Fw79th4x0X14yowQQgghhBBCOkYsFuOl1Q/g2dfeNsT2HUnAuYtXMXhAXx4zI/ZsYL9eGNiv9aoYvV5/a0JXLbS3xic1zLIGGo361vKtwshb20glEvSICIaTvOOFWMYTUIlF/F4mE+rvwSznl9dDq9XZVeFddW0Dth46i5zCMpN1UokIM0YPQIVSjNTyLEM8wF0GiUgAqE12sYqKWrZzo6dr1+rksy6pAvUq9pyit6sUf5obDQ8X25vge2y/QAgEHDbGZ7a4Pq9Sif1XyjEjxqeTM3NMYb7OuJR1W3GjA3ZuzCll71OoryuvnUoJIcQRcByHMXGx6NurG37cthdpGaaFhxqNBknnLiLp3EVwHAc/H++myV5DAhESHAhnJyeIRSIIhQKoNU3NHiqrqplJXmtq2/67xHEcJo0egtmTRjrUtTx82bjtd6x86hWTwsZHVizGl+/+o2nMmSZ6JYQQQkgnoOJGQm6JG9QfE0cPw+ETSYbYfz/7Hg/fN98wmyAhjkStNh054vsLf3hwAC5dvW5YzsnL5zGbO1dZWYWjx0/i4uUUk3Ucx2HsqBEYO3oEhEIhLqWkMetlUgl8vT07K1VU19SaXBzfVYob9Xo9ftq6Fwol27H0oeWL0b1bOE9Z3ZlAf1+sfngF3vv0W8PvT6fTYd2W3Xh59f0QiYQ8Z+g4wkPYQj+VWo3zyVcwfOhAnjKyLLVajTPnLzOx8JBAnrIhhBDH5efjhacfuheFJWU4fvoCTp270uIEGLdrbFSgsVHRoeNFhAZibNwgDOof1fSZvgNdDAirsqoaUxetwtkL7N9NXx9vHP5tA/r1jeIpM0IIIYQQQggxz8P3LcB//vcVSsqaixr+75M12Pzt+zxmRRwZx3EQiURtj7nrzevu2BZb69wY5u/OLGu0ehRWNiLExz46il25novd8clQqEzHmkP8vbBw8lB4ujlj/eHrzLown84tLjQubvRys80Jia0ho1SBczns/XdzEuPp2X1tsrDxD6P7BqBRqcH20zktrt9zuRz9Q10Q4mkbXSftWaivC7OcU1qHBqUGTlLHuTYqLb+aWQ7zc+MpE0IIcTx/dHFMupCCQyfPIq+wpMXt9Ho9ikvLUFxahjMXLpl9XI7jENO7O6aNi0NEKF3nYgk/btmF+5951eRawsfvvwefvfM3mkyXEEIIIZ3Kcc5KOAJzLz7U6804trkfQs04tjlEZp601LEDGa88/QBT3HgzJx+bt+/FfQumm+6rafvC1HYJJWbsbM7jbe5Frubsb+6xeXqemcus16b1LkpWtTDgJL69uNGcvDsozKgDXV5+ETQajc0WGBcWFiHh9BmkpF5rsZuOh7sbFs6bjbDQEEMsO5ct2AwN8u/UGQIrKquYZblcBrkZs93ak4Szl0y6Jw0fMhAD+tlHx52IsBBMmzgGew4eM8Tyi0qx50gC5kwZ0zlJdIFCCRdnJ/TuEYG0jCxDLD4hyWGKG89fSkF9QwMTGxLTm5f3fEJ41QXez4htCPTzwT1zpmDu1HE4e/Eq4k+db3WA726JxSIMiemDccMHmXYCp/f1u6a/bcbRisoqTF10P85fZCcv8ffzwaFt69G3d09mewsc3JydLZZGp+LzHJi56PVFCCGEEELsnFwuw58eW4HX3vrYEPt190Fcy7iJqB7deMyMEOtQa4yKGyX8TvbqLJPA280J5TXN5+pzSutsurhRr9cju6AMJy6k40Zuscl6jgPGDu6DsYOjDBdAm3RN8+7c4sLyanYsxKuLdG7U6fX4+qRpR80HJvWCr7vtPwaTY4ORXVqH5MwKk3U6PfBjYhH+PCMcAjrHb5YIPxcIBBx0uqbzXBqtHqfSSjBxQBDPmVlGQUU9bhTWMLHugR78JEMIIQ6K4zjEDYzGsNi+yMotxLHTF3D+8jVotJaftMTNxQmjhsRg1NAYeHlQsbqlrNu8HQ/96e8m1zyufmgZPnnrVXD0eYsQu8JxHL1uAXoMCLFztlktQghPJo4eiqGxfXEm+aoh9s6n32PpvKk0CwlxOMYDeQD/g3nGF2Wr1GokX0nFkNj+PGVkSq/X42ZWNk4mJuHGzaxWt+sf3Qezpk+BTNZcOKjT6ZB0np2JKqyTO6aVV1Qyy12la2NldQ1+3X2IiXm4u2HxvBaK123YjMnjcCklDfmFzQPH+44lIja6F0KNioNJx40dPogpbszKzUd2bj7CQ4P5S8pC4hOSmOWo7uHw9/XmKRtCCOk6ZFIJRg+LxaihA1BeWY2c/CLmp6Gdjo0cB/j7eCMsOABhwf4ICwpAaLA/pBJzJs0hLSmvqMSUhauQfDmViQf6++HQb+vRu1d3njIjhBBCCCGkFbxNvmDmhSJmT7zaUeY+XjzlLTKzIEXDfu9cvWI+/u+TNaiprQfQNPbxf598i+/e/3sLxza3y5c5Y09mPM/MfY5xQjN2ts2JT9s/tpmPmc563RfNoVKzE76KLTmpagcfszB/D6a48XpBDUb2sb1xHp1Oh2tZhThxIR0FJZUtbuPh6oQFk4YgLNDHEKtpUKGoiu0cGObvCojEJhNQ3xXRnZ8Lq6xXAYLm17GXpxvg5N7GHu1orGl/m9bIzbwI/y7eh3cml6FBxb4HjY0OQK9gM+57J+I4DveOiURGQQ3qFKbPldwKJdKKlegb7Nr2DZkz2bg5z1FzJ0Qz531Uazq5dWucxUBshCfO31ZEevxqEcbHBDpE4ejxFLYI21UuRr8wN0Br5iT2DHM/E3b8uaLXmnFszszHwJzPCgLz/v5y5ny+kri0v02rzPz+YFb/AjNej+Z+T6TrJDuXjTZOaJ8eHAd0CwtEt7BALJo5HonnLuP85XTkFZW22CjgTsmkEnQLDcSIwf0woE9PiERNn+v0dj8BpRn5m/s547b9v//5Nzzywusmj+czj9yHD994uek99/Z15j7u5uRu1v2202Ys5n43t+QkvYQQQkgnouJGQm7DcRxeeeYhLHz4JUPsStoNbP39MBbPnsxjZoRYnkqlYpY5joNQaM5ArflcXZzRIyIEGVl5hlh8QpJNFDfqdDqkpqXj5KkkFBQWtbqdVCLBrOlTENM/2mTd1WsZJsWFg/pFWTzXthh3bvTuIsWNB46dhkLJPufvWzwXTnLbn6H0diKRCKuWLsT/ffSV4SSgTqfH7kMn8MTKRTxn5zj69+4BD3dXVFXXGmLxiWew0s6LG3PyCnAzO4+JjY1zjI6UhBBiLziOg4+XB3y8PDCof28ATYNw5ZXVKKuoglqtgVqjgVarhVgsglgkglwmRZC/L2RSKmS0ttKyckxZeD8upaQx8aAAfxzevgG9qIMJIYQQQgghxEG4u7lg9f1L8M6nPxhiG379Hf984VGTiSgJsXcqlVHnRjH/l8l0C/LChesFhuXkzHIsGhUBFxm/E9H+QaPR4mJ6DhKTr6O8uq7V7fr3DMXMMQNMzlslppUw1z+LhRxCOrFzo0KpRoOSLfTycu3czpF8qGrQ4FBqFRPzdpVi3vBwfhLqIFe5BPeMjsR3B9NbXB+fVtF+cSNp19i+fkxxY2m1AtfyqtEn1IO/pCxAodIiKb2EiY3sGwCRkAq1CCHE2lydnTB1bBymjo2DWqNBQVEZsvOLkFtQjJz8YpSUV0Kl1jAFdUKhADKpBEH+vggL8kNYkD9Cg/3h6+UJgcD+C+5t0ZqftuKxF/9lUtj43OMr8f4/X6KuZ4QQQgjhDf9nbQmxMXOnjkXfXpG4mp5piP39v19g/vTxEFlyFkdCeKZSsQM6EonYJr6cjo0byBQ3ZmRmo6CwGEGB/MxWqlarcfFyChJOJZkUBt5OIhFjcOwAjIgbCje3lgdTjDumhQb5IyI0yJLptsv4PnSFzo0KpQqnzl9hYsOHxKJfn148ZWSe0OBATJs4BnsOHjPELqfdQEVVDbw8zJz5lQBoOnk6Zlgsdh44boidPX8Jc6ZNhIe7/T7Gh+ITmGV3NxfE9OnJUzaEEEL+cHvBo4Hdzzpqf0pKyzF5wUpcSWUvmgoJCsDh7RvQIzKCn8QIIYQQQgghxEqee2QpPvzmZyiUTR18NBot/vXBN1jTUvdGQuyYWsMWN4ptoLhxSO9gbD+eAq2u6RyQWqvD6WslmDSA30kWFUoVzqbcxOnLGahraL27l5NMgmmjYhDTK8xknU6nx4mr7ESxg7t7QyLqvEl2K2rZrpEcB3i6ygG9GR357MDRa1XQGZ1WXD6+B6Rific47oiB3b0Rm+mF5NuK7/6QkleL8joVvF1oMjhzdA9wQaCnHIWVza+X4ylFdl/cmJReAqW6uVsRxwGj+gbymBEhhHRNYpEI4SEBCA9hJ8/R6/XQ6XTQaLUQCYUQUJfQTvX1+i144s//Nom/+OT9+O8/XrCJa0cJIYQQ0nXRJ0NCjAgEAvzj+UeZ2LUb2fh+006eMiLEOowH8iRi25gJNDY6Cq7O7MyZ8YlnOj2PxsZGxJ9IwIeffoVde/a3Wtjo7OSEieNG4/mnn8C0KRNbLWwsK69EStp1JjZ2+KBOPynQFYsbz168arg4A2gqIJg1dSKPGZlvyvjRkN42C65er8eJpGT+EnJAI4cOYE6iqjUa/LRlh8nMZfYiJe06zpy/xMTGDI2FkGYpJYQQQlBcUoqJ85abFDaGBgfiyI4fqbCREEIIIYQQ4pD8fLzwyH3zmNjaX3Yj9fpNnjIixDpUatvr3OjmLENsD/ZC7+MpxdDxNAZRU9eI/QmX8eGGvTh0OqXVwkYPVyfMGD0Az62Y3mJhIwBcyalEZZ2KiY3p62fxnNtSUdPALHu4yB1+PESj1eNERg0TGx7lh17B7jxlZB6O43DvmEhIRKa/Nz2Ak+mmRY/k7nAcZ/LavJxdgeySWp4yMp9SrcXB5AIm1j/CG16uMp4yIoQQYozjOAiFQkglEips7GRf/LCpxcLGl595mAobCSGEEGIT6NMhIS1YPHsSBsf0YWL/+uBrNDQqeMqIEMtTqdhBJbGNFDeKREKMGjqAiZ0+l4z6hoZW9rCs6uoa7DtwGP/79EscPnai1eN6erhj1vQpeO7pxzF29EjI5fI2b/fYydNMUZRcJsWQAX3a2MPydDodKqvYQS1HL27U6/U4lniOifXr0wvet3dGskMymRRxg2OZ2MkzF6HRaPlJyAG5u7ogbmA0E7uSmo6k8xd5yqjjGhsV+PGX7UxMKpFg1LABrexBCCGEdB2FRSUYP+c+XL2WwcTDQ4NxdOdP6N4tnKfMCCGEEEIIIcT6Xn3mATjJmy/41+l0+Nv/fcFjRoRYnkqtZpZtZcLXsTFscWBZjQJpeVWdmkNpRQ22HzmHj3/ch8SL16FUtdzZ0N/bHQsnD8Uz903FsP7d2+x+eTyF7doY7uuMcF9ni+bdnnKj4kZPV6dWtnQcF/PqoLitWx0ATIix7251rnIJhvb0bXFdwvVKqLW6FteROze0hzek4uZLB/V6YP2RDKg19vnY/nYqGxV1bHH2mGj7fh0QQgghlvDpmh/x1F/fNIm/+tyjeOvVZ6mwkRAHwXH0Qwixb1TcSEgLBAIB3n7laSZWUFSKT7/fxFNGhFie2niWUoltDOQBwOhhscyXZqVShV9+22PVY5aUlOK3nb/jo8+/RmLSWahU6ha3Cwzwx5IFc/HMk49i6OCBd1QUmptfiCMnTjGx4YP6QyqRtLKHdWi1Wuh07ECE8bKjyczJR35RKRMbO3IYT9lY1tgRQ5nl2voGJKdc4ykbx7RgxkS4ubAD7pt/+x1V1TWt7GGbft251yTnhTMmwN3VhaeMCCGEENuQX1CE8XOXIe36DSbeLTwUR3b8iG7hoTxlRgghhBBCCCGdI8DPB88/dh8T27b3KE6du8xTRoRYnvGYqFjEf+dGAIgM8kSwjysT23c+v1O6N+YWlWPjnkR8vukgktOyoW1lvDAi2BcrZo/C40smon/P0Ha762QV1yI1t4qJdXbXRgCoqmtklj1d256k1hEcvFrFLHcPdEOwd+cWlVrDmOiAFuN1Ci1S8+s6ORvHI5cIMWUAW/xXVNmIPedyecqo467lV5sUV3cPdEPvUE+eMiKEEEJsw0dfr8ezr7xlEv/7C4/jjZefpsJGQgghhNgMKm4kpBWTx8Zh0mi2AOadT38w6XpGiL2y1VlKAcDLww0D+0UxsaTzF3EpJc3ix8rJzcNPm37F5998j+RLV1ot9uveLQKr7rsHjz20CtF9e7c7ePcHjUaDdRu3MrcrEAgwbsQgi+R/N8RiMXx9vJiY8YXcjibhDNtlz8fbE316decpG8sKCvRHj0i2k9DJM5d4ysYxuTjLsXT+NCbW2KjAT1t2MJ1YbVlK2nUkJJ1nYr17hGM0dW0khBDSxeXlF2L83GVIz7jJxCMjmgobI8JCeMqMEEIIIYQQQjrXS4+vgLenOxN75e3P7OYcKCHtUZlM+GobxY0cx2GMUffGG4U1JsU5lqLX65GeVYjvth3Dd9uO4VpWYSt5AX27B+ORheNx/9wx6B7qf0cXPKs1Omw4msHEnKRCDIr0amUP65GIhMxySaVjF8EVVimRW2ncra7lokB7E+LjjEh/1xbXZZU1thgnd2fKgACEeLPdTQ8k5yO7pJanjO6eUq3FT0bvP2KRACsmRlHBBiGEkC7tgy9+wPN/e8ck/s8/r8a//vIU/Z0khBBCiE2h4kZC2vD2q2z3xqrqWvzf52t5yoYQy1KpVMzynXQg7EyLZ06EXCZlYj9t2YH6hgazb1uv1yPt2nWsWbsB3637CekZLRf4cRyHfn174/GHVmHlffcgslvEXX+p33soHvmFxUxs+vgR8PPp/IE8AOjTqweznHotw6EvUMi4yc4qOXr4kDsuTLUHY0ewRfiZOfnQah27G2dni43uhaED+jKxK6np2L3/CE8Z3bnCohL88NOvTEwqkWD5ghl0gpIQQkiXlptXgPFz70NGZjYT7xEZjqM7fkJYSBBPmRFCCCGEEEJI53N3c8ErzzzAxI6dOo+9RxL5SYgQC1NrjIobbWhMdFjvYHgZdRXcfiobZTUKix1Dq9UhOS0bX24+hJ/3JCK3qLzF7URCAQb37Yanl03FkqlxCPa/u7HM38/loqiSLTabFBMAiajzx+W6B3szy3mlVahtULaytf07l8MWb7rKxYjtxs9YtDW0VqiZW07FjZYgFAiwclw3CAXNY4d6PbD+SAaUai2Pmd0ZvV6PrQlZKK9lX+PzhneDr7vjd20lhBBCWvPfT9bgpdffNYm/8den8Y8Xn+AhI0IIIYSQtjnO1f2EWMGQAX2xZPZkJvbxmo3ILyzhKSNCLEdtMkup7QzkAYCHuysWz5rExGpq6/DLb3vMut3snFx89tUabNyyDbl5BS1uIxIJMWzwQDz75KNYvGAuAgM7NrNlbn4h9h6KZ2LBAb6YPmFkh27PEvpEscWNlVXVKC4p5Skb62poVKC0ooqJGRd32rvevSKZZbVGg8KSMp6ycVxL5kyBm4szE/v9wFEcik/gKaP2lZVX4uOv15oUhC+cMcFkFnZCCCGkK8nOzce4Octw4yZb2Nirezcc2f4jQoIDecqMEEIIIYQQQvizetVihAb5M7FX3/kMOh1Npkfsn3HnRrFRVz8+ScRCLJ/Sn4mpNDpsOJIBnZmTkypVaiRevI6Pf9yH7UfOoaSipsXtZBIxRg+Kwp9WTMfscQPh5e5y18fKKq7FweR8Jhbq44TJMfx0D4wM9IJU3NyhU68HruU45ngoAOSUs0VdQ3r4QCR0nMvBYrt7t1gkm1Pe6NCT+HamYG8nTB/CdpItqmzE13vToLbxiXX3nc/HyVR2sunugW4Y258msCOEENJ1vf3RN/jrGx+YxN967U947bnHeMiIEEIIIaR9jnM2ixAreeMvT0IobB7gUCiU+Nf/vuExI0IsQ6VWM8u2NEvpH4YP6od+Ud2ZWNL5izh2MqlDt1dQWIR1P21GWXlFi+vlMhnGjR6J559+AjOnT4Gnp0eHjgM0FWJ+u34TM/AvEAiwasksiHgcNA3w94OHuxsTS72WwVM21pWTX8Qsi0QiBPr78pSNdbg4O8PL6HmaW1DU8sakw1yc5Vi+cAaMmx3+umMvDhw5wU9SbSguKcP/vvgO1TW1TDw6KhKjhw3gKStCCCGEfzezczFu9lLczGa7e/fu2R1HdvyI4CB+LrgjhBBCCCGEEL7JZFL866XHmdjFq9excft+njIixHJUKtseE40K9cbo/qFMLKOwBvFXOj7eU1Jejc83HsT+hMuoqW+5u52bsxxTR/bHcyunY1JcNFycZB06lkqtxYajGbi9xkwo4LBiXDcIBfxckiQSCdEjhO3emJrtmBNY6/V65FSwnT4j/F15ysY6xEIBQnycTeL1Si0q69Ut7EE6YurAUIT4sMXN1/Kr8f2BdGhstMDx6OVC7DqTw8TEIgFWTIyCwHhglxBCCOki/vPBl3jtzQ9N4v/39+fx12ce7vyECCGEEELuEBU3EtKOXt3D8fCyeUzs+407cC3jJk8ZEWIZKpWKWRbb2EAeAHAch/vmT4NcJmXim3/bjaTzF+/qtpRKJbZs2wGtVmuyzt3NDdOnTMTzzzyBCeNGw9nZdHDkbjQ0NOKTr9eitIwtopw+fgRCeb5gmuM4k+6Naddv8JSNdRkXNwYH+kMkErWytf0KC2FnnczJL25lS2KO/n16YNn86Sbxbbv347ffD9jMDOY5eQV4/7M1qKyqZuLdQoPw8NJ54GggjxBCSBd142Y2xs9ZhuxctotA36ieOLLjJwQG+PGUGSGEEEIIIYTYhpWLZqBvr25M7B/vfWUynkSIvVFrjDo3im1vrGj+6N7wcpUzsa0JN3Epq+UJW9uiVKmxae+pVosafT1dMW/CYDy7fBpGDOgJqaTjY8QarQ7f7r+Gokr2WDMGBSHYy6nDt2sJfcLZcz1ZhRVoVDpeIVxVgwZ1SnaMKszXvLFuWxTm23JH0Zzylp/n5O4JhQI8OKU3XGTse8KlrAp8vTcNKrXpdRZ80ev12Hc+D1tOml63dd/4XvB1l7ewFyGEEOL4/vXu5/jHO5+YxN//91/w56ce5CEjQkhn4TiOfm79EELsl42ctdWDmcbNXF31jcms+23uY2ZGnazejBNgAjO7n2k17W8D4B/PP4L1W3ajUaFs2k2rxd/f/gibv3mvw4fWazt+4pwTdWzWxKad7+x3rVKpkVdYjMLiUiiUKqjVamg0WgiFAkjEIkgkYvj7eCM0OMCk8KxVlnyd3zVzju2Y7ylqNfv8l5gxcGVNHu6uuGfOFKz9ZZchptfrsW7jNuj1QNzg9ruQ6fV67NqzHxWVVUzcz9cHo4YPQ7/oPkyHVnPU1dfj02/WI7+QLTALDfLH9Akj20rSIse/E316dkdi0jnDcnFJGcorKuHt5dlpOXQG4+JG4yJARxEWEojky1cNy8b322Hw+jekyeihA9DQqMBve48y8f2Hj+PGzWysvHcB/Hy8W97ZyrRaLfYdPo49B4+ZFHEHBfjiqQcWQyYVw7y/h8R20eecLoPP90Kznipd9Hlm1u/L3N+1nT7mZjxmen3rEw1kZGZh4rwVyDPqcN2vTy8c3LYefr5m/v1u49jWZ6e/a3Of41313B8hhBBCCCFWJBQK8ebLq7Hg4T8bYpnZ+fh63WY8/cgKHjMjxDwq4zFRGyxulElEWD6lPz7ZmmSI6fTAdweu4ckZfRAV4nFHt6PX67ErPhkVNfUm60IDvDF6YC/0DA+wyMV+Wp0e6w5fx9XcKvY4Pk6YMoDfyV4BoGeID0RCgaHjnE6vR3peGQZ0D+Q5M8vKqVAyy3KJED5uZlxPYqNaK9gsrFIiNryTk3Fg/p5OWD27Hz7efgmK24oZr+ZW4dNdV/HA5J7wcuX3+aVSa7HtVDaOp5iOiS8Z0wNDe9EkdoQQQroevV6P1//vU/zngy9N1v3vP3/Fnx5bCehsZ6ICQgghhJCW2N5ZW0JsUFCAL559ZCn+79O1htiWXQdxJvkKhsb24zEzy9Dr9cjJL8TNnHzk5BUiJ78IRSWl0Onu7GJDPx8vhAUHICw4EBGhgYgMD4FAQI1hbZ1KzRbYim24o13cwGiUV1Rh16EThphOp8Pan3+FQqHAuFFxbe6ffPEyLqekMrGQ4EA8uPI+ixU1AkBlVTU++XodikpKmbivlwdW378EIpHljmWOkOBAuDg7o66+eWAzLT0Do4YP5TEry8svKmGWHbe4kb1feUUl0Ov1NAuNlUwdGwcBx2HrniNM/MbNHLz5/ueYN3Myxo+K69S/g3kFRVi/aRty8wtN1oUFB+DpBxbDSe54A9mEEELInUjPuImJ81agoIidfCQmujcObF0LX54mJiCEEEIIIcRieJ381AxmzXfB40RPvD1kZh5Yd2cTvs6dMhojh/RHwtnLhth/3vsMD9wzGy7OHezCJjBjck0zJrrluLbP0VbV1CEnvwg5BcXIyS9GcWk5lCoV1BotNFotREIBRCIRpGIRAny9EBrkh7BbPx5uLXcQM9Dw2e2SJrgzZlrcaMEJXy044VFUqDemDI7EgXOZhphGq8cXe1Lx8JQo9I/wavc2ktOyceV6LhPz93bHrLGxCA2w3DkQtVaH7w+km3SWlEuEuH9iTwhFktZ3bue12SbBnY9nS6QiRIb4Ij2nedw2Nb8aA3p3a2Ovto5txjiv2MwucsLWn7O5VVXMcqivi0OOEYb6tPy+q9S08hq04DUAnUpsxu/uLl4fLbr1WSHMR44nZvTG57+nQnXb45tZXIs3NydjwYgIjOrjz8vz7EZhDTYczUBptcJk3dyhwRgX5Q6oTIu7W6VWtr9Na4RtvM/dCZEZf4vMeR81lznHlpjZVdac55yYx462fL0lm/saNWcCT7Pvc8dvQG9G3ma/rZn1NZXP7+bmTDxq7ncPe/3uYkbe5jxmrXz30Ov1+Nvbn+Dtj74xWffJ26/iqYeWNe1rhWN3CnP+9pidtxmvTV4/kvP0WaHF9zLH+25CCCHEemy3koUQG/Py6vvx9YZtqKyqMcReefNjHNj8ld2eHG5sVOD0hcuITzyHopKyDt9OSVkFSsoqcPZiU+cwLw83jI4biJFDBsDNtZ3BPcIbtVFxo612bvzDjIkj0aBQ4PDJs0x807bdyLiZjXsXzIKLs+kJydLSMvy+/yATk0mlWDx/rsUKG/V6Pc4mX8bmbbtR39DIrHN3c8EzDy+Fe3sD3Z1IIBAgqkckzl1svjgh1QGLG+sb2EENHwfrTPkHHy92MFut1kCj0UJsgzMPO4rJY4ZBLpfip237mJO1arUaW7bvwYVLKVi2aC6CAqw7M6hSqcKh+IQWuzUCQM9uoXhi5cI777BMCCGEOJi09BuYNH8lCovZSS9i+/fB/l/Xwse7/YsCCSGEEEIIIaQr4TgOb//1KYxb/IQhVlJWgf99tQ5/f+GJNva0fVqtFpdSM5CUfBVZeYWorm27+KFpGE2FWgBllTW4kp5lWOfm4oSIkAAMjYnCgD6RFp1Ik1iWXq+H2qi4UWwjk5G2ZO6oXqhrVCHxap4hptHq8c2+NNwzJrLNgqLSihrsOXGRickkYiydPhwebpYr6qhtVOGHg9dxLb+aiUtEAjw5ow8CPDtYCG0FfcL9meLGG3llUKk1Ntm9s6NKathi6hAfHgt4rMjfQw6xSAC1UTGj8TKxjB5B7nhmTjQ+330VjarmMUilWoeN8Zm4cKMcy8d377Qujiq1FrvO5ODIpcIWy0kWjwjF+Gjq2EgIIaTr0ev1+Ot//od3P/3eZN3n//07nrj/Hh6yIoQQQgjpGMc5Y0eIlXm4u+KvTz+Al//zsSF2+EQSDhxLxNTxI3nM7O7lFRQj/tQ5nLlwGUqVuv0d7lJFVQ127DuG3QePY2C/3hg7YjC6h4fQHBw2RqUyLm40c2Y5I9XVNbh89Rr2HY5HoL8f5HIp3N3c4ObqAnc3V7i7ud76vxtkMmm7RcIcx2HRzIkAYFLgeC75Cq5dv4lli2ZjYEy0Ia5Wq/HLth0mg5bzZs+Ah4e7Ze5nTS02bt2Fi1dSTdZ5ebjh2YeXwsfLwyLHsqQ+UT2Y4sa8/CKo1WqILTlbLc9MBqsdaKDydi3dL7VG47D311aMGjIAvl6eWP/r7yivZAfwb9zMwX/e+xRRPbph7MhhiInubdGLW4pLyhCfmIRTZ5LRqDCdmZTjOEwZG4dZk0badFdeQgghxJqupl3HpAUrUWw0kc+gAdHY/+taeHl68JMYIYQQQgghhNi4MXGxmDVpFHYfOmmIvffZD3hi1T3w9bG/SWKqaupw8sxFnDhzCdW1dRa5zZq6BlxKy8SltEy4uzpj5OBojBocDU93V4vcPrEc47EiwDoTvmq1WhSVVUGn08HNxQmuznIIBHffLYPjOCyb1A9KtQbnrxcZ4jo9sDE+E5ezKrBsbHd4uLCTGqrVGmw5kAS1hp0Icc74QRYrbNTr9Th/oxybT2SiXmE0Bifk8Oi0KEQG2NZrICrMDwKOg+7WRJUarQ438svRJ8Kf58wsR6Vli/ucpY45LiQQcJBLhCbFjBqdvXZ8sn3d/F3x3Lx++HJPKirr2CLaa/nVeHNzMqYODMHIPv5wlVvnGgO1VofkG+XYcy4XJS10axQJOSwbHY64npbrTEsIIYTYC71ejz//63188MVak3VfvvsPPLZqCQ9ZEUIIIYR0nGOe1SLESp5+YAk+WbMReYXNXR9eefNjTB47vEODE52tsroGP/+2F1dSr9/R9hzHIcDPB25urhCLRBCLRNBotVBrNKirq0dhcWmLnaL+oNXqcPbiVZy9eBXdwoKxfMEMBAX4WuruEDOpjDo3mlsAo9frUVRcimsZN5CWfgNFJaW4fiML+YVFiBsci/LKKjjJ5fDyNC0qFIvEzQWPbi63Ch9vL4B0hUQiMRQ4Osll2HXwhOG4Op0OlVVV+PSbdejftxdmTBoHqVSCCxcvo6SUvZh56OBY9Ondy6z7+sdxz164jM2/mXZrBAB/Xy8889BSeHm4mX0sa3B3Mx1cvL0DnSNQa4yLGx2ncPN2Ld0vtVoDyHlIpovpFRmG1559CNv3HcOxU+dN1l/LuIlrGTfh7uaK0cOHYOjAGPj6eHWo43NjowJp128gPvEMrl3PbHW7QD9vrFw8CxEhgUCLc5cSQgghju9KajomL1iJktJyJj4ktj/2/foDPC000QkhhBBCCCGEOKq3Xn4Svx9OMIwb1NbV462PvsH/3niZ58zuXFZuIQ6cSMLFq9ehs2LxS3VtPfYcTcK++DOI6R2JyaMGoVsgFVnYCuOxIgAW69qnVKqQkZ2La5l52H3kNFRqDbqHBUKr00EkFMLVWQ5XFye4uzrD7fZ/b/3fWSZqcbxAIOBw//QBEIuEOJ2az6xLyanCm5uTsXBkBIZH+Rn235dwGSUVNcy2Q6Ij0bd7sEXua22jCpuOZyI5s8JknUwsxOPTo9AzyPbOt8ilYgT7uSO3uMoQK6tuu2urvVFr2fc3icj2r1vpqJbum1BAU2xbU7C3M15dEoutiVlITCth1inVOuxMysGes7kY2N0bY6ID0M3ftUPjoMbKaxQ4kVqMxNRi1ClM38cBINzXGSvHd0eAu2NeA0AIIYS0Ra/X44V//Bcffb2BiXMch6/f/yceXr6Qp8wIIYQQQjqOihsJuQtyuQyvv/gYHn3pP4bYhStp+GXnAdw7bxqPmbVNr9cj8exFbNl1EAqlstXtfLw90T0iHGEhQQgLCURIUCCk0ta7+ak1GhQUFiMnrwA5eQW4mZ2LgqKSFre9mZOPdz79HjMnjcaUscMhFDruSXV7oTYqbtx76BjSrt9ARFgIIsJC4O/r0+6JZ41Gg6ycPFy7nolrGZmoqa1l1gcG+EIg4FBZXQOpRIKcvHx4eriZ3K5ao0ZZRQXKKkwHxP4gl8ng6uJi2F6sV+L85Wsmz+lLly7h581bERTgh/DQYHi4NxcXBvj7YdrkiW3ep/Y0NDbi9NmLiE9MMukC84dB/Xtj6bxpcHG23eqy6hr2d+XkJLd4906+CQQcdLdN3qnT6Vrf2I61dL/soeDeUcikEtw7dwpio3thw9Y9Jl0cgabX2+79R7B7/xHI5TKEBQciNCQIYSFBCAkKgFwmg1gkgkAggFqthkqtQVlFBXLyCpCbV4jsvHyUlrX+/gjc1q1x4ijq2kkIIaRLu3z1GibNX4Gy8komPmzQAOzd8j3z/YAQQgghhBBCSMv69+mBFQunY/2vewyxL37YhOceW4nw0CAeM2ufSqXGjv3HcCThLNqb01EqlSA0OBBhIUEIDQ6Cu6sLxGIRhEIRtFoN1GoNaurqkJtX2HS+Nr+w1bFWnU6P5Ks3cDH1BsYN7Ye5E+MgtUKHQHJ3VC10bswrKkW3kMAOdXCsqa1HelYe0m7k4mZeEbQaNbRaHUrKqyGXSlBT14DM3CL0jAiGXq9HTV0D8otaHk8UCnCry6MT3F2dDAWQ7i5OkMskGNnHD1VVVUi4kgOdTmf40Wq1eDvjJoK95Ijt5gVXuRDXs4uY2/bzdsO0kf3v+v4ZU6m1OHO9DDuTslssMHKVi/HkjN4I83Ux+1jWojHqbCiXOtbrUmtUvC1w4GI/YQvjnxK67sTq5FIRlo/vgYGR3vjp2A1U1bNdHDU6Pc5cL8OZ62UI9nbCoO4+CPN1QaivM1xkd/Z6U2m0KChvQE5pHa7mViElu7LVKVxFQg6zBodgYkxgU3GrruXiR0IIIcRR6fV6/Om1d/Dpmp+YOMdxWPPhv/HA0vn8JEYI4RcHwAITjdg9eggIsWt05TMhd+n+JbPw/pcbkJaRZYj9/f8+xcKZE22yK1hldQ1+/HU3rqa33OVJIBAgJro3xo0chl49ut3VLGpikQjhocEID22e8TErJx/xCUk4l3zZZCZMjVaLHfuPITnlGlYtnk1dHHmmNRrIUSiUSEtv6roIAHKZ1FDo2C08FL4+3uA4Dg0Njbh+4yauZWQiIzMbKrWqpZsHADQ2KpGbXwhnJydoNBooVSoUl5QhwP/uf/eNCgUaFQrDsqe7K0YNjcGVtBsoNir60el0yCsoQl5BEdzdXA3P08Xz50DUgQ6Ver0eeQVFiE9Iwpnzl0y6Xv7BxdkJS+dNxaD+ve/6GJ3NuLixpU6O9k4sEkGjae4ua1zQ6yhaul9U3Nb5orqH47VnH8K+Y6dwIim5xY6uQFMHxj86OloCxwF9e0Zi9uTRCA8JtMhtEkIIIfaqvr4Bc5Y9alLYOHxILPb88r1DfuYlhBBCCCGEEGv55wuPYuOOA1DfKg5TqdR4/b+f4YdP3uQ5s9bdyMrD+i27UWL0vfB23cJDMXLYIHTvFgY/H+87mixw2KABAJrGn0rLKpCZlYOEpPO4kZVjsq1eDxxNuoIr13Owcu4E9Ain87Z8UqlMx1B+3nEYAgGHYH8fdAsNRESQL0IDfVscW9Hr9Sguq8S1zFxcu5mHguJyk22EQgGC/LxwM68IoYG+EAo43MwtQr9e4W3mptXqUFldh8rquja38xQ14srNEijVWiZeXAycT82Cu5ME4X4uCPSSQygQQCwSYsmUOIhEwjZvty3FVY04kVKEU9dK0KjStrhNbDcv3DsmEq5y27tG4nY1dQpm2d1ZxlMm1iEyKmbUaK3XqZZvxoWqgGN3qrQ1fcM88do9sdiWmIWEtJYnPc8vb0B+efPfRm9XKUJ9nRHk5QypWAiJSACOA9QaHVQaHUqrFcgtq0NhRQPupMmyoVujp+1OMk0IIYRY24uvv2tS2CgQCPD9x//ByiVzeMqKEEIIIcR8dOU7IXdJJBLhPy8/icWPvmyIZdzMxbc/bcOT99/DY2amzl5MwU9b97Q4g6iLszPGjhyKUXGD4enhbrFjRoQFIyJsARbOmYZTZy7g2MnTKK+sYrbJyS/CO59+j3nTxmPi6KF3VVBJLCcsJAiVVaYdxv7QqFAgNT0DqekZAAAnuRyeHu4oLC654w54IcGBUGvUaGxUQCKRQKlUolGhQIC/L+rrG1FXXw99e9PmtkEmlWBwTB8UFJci5VqmSUEt0FTEdyklDXWNKny1duOtzqRNP8FBAZAYFSXr9XpUVFYZOpI2/RSivqGhzVwG9e+Ne+dOhauLU4fvT2eqqq5hlh2xg41ELEajovn97/b/O5KW7pe4A0W8xHwyqQTzpo7FzAkjcf5KGuJPX8DNnAKrHMvZSY6RQ2IwZlgsfLw8rHIMQgghxN68++k3yMlj//aOHDYIv29aAzcqbCSEEEIIIYSQu9ItLAhPrFyIT77bbIit/2UnXnzyfvTv24vHzEw1dWuMx5GEMy12a5SIxRg6KAZjRw5DaHDHiw0FAgH8/Xzg7+eDEcMGMRNjKlXsZKBllTX4cO12jI/rj7kTh5mMR5HO0Vp3Rp1Oj9zCUuQWliJer4NQKEBIgA8iggMQEeIPnU6PazdzcS0zD9W19e0eR6FSQaFU4VzKdUOsuq4B7hYYN/T3dIaXaziuZpciv6zWZH11gwqXsiqQmitAiI8T7pk6DFLZ3RXwaXV6FFc1IKekHmeul+JafutjyM4yEe4Z1Q2Dunvb/Di/WqNFvYJ9bXq4OlZRlNioc6Fac2fj+PZI1cJ983eX8pBJ1yWXinDf+B4Y1TcA8VcKce5GWZsFteW1SpTXKpGcWdHqNneiV5AbxvT1w4AIL4fuTkoIIYS05+jJM/jwq/VMTCAQYN2nb+G+RbN4yooQQgghxDLoyndCOmDBjAmIG9Qfp89fNsTe+OBrrFoyB85OtnEy/MjJM/hl5/4W18UNjsWSeTPgZMVcXZydMHn8KIwdNQy79h3GoWMJTBGbRqvFr78fQmV1DRbNmmTzAx+OaMaU8XB2dkJa+g1UGBWgtqShsRENjS13Irudl6cHonpEIqpnJMJCgpGRmYWftmxntont1xfDhw6CVqtFbV0dqqtrUV1Ti5raulv/Ni+3d0yOA4IDfOHt6Y60jGwUFJeC4zgIBAIIBAKIxWL4+PjA29sb+YXFyC8sRuKZC4b9hUIhxGIRBJwAao0GGo3mrgou/X29MHfKWAzsF2VXbd27QudGL093VNc2z3abX1iE/n2jeMzIOvILi5hlDzcXCIU0SymfxGIR4gb2Q9zAfsgtKEb86Qu4ePU66urbLpJuj1AoQERIEEYPG4BB/XpTh05CCCHkNvkFRXj3k2+YWNzgAdiz+Tu4urrwlBUhhBBCCCGE2LfXnnkA32/aZTi3qdfr8be3P8H29Z/wnFmz6to6fPb9ZuQVmnaREolEmDF5HMaNGgYnueXHRUOCAnDf4rlYMGsq4hOT8Pv+o8xEnHoAR05fxrWb+Xhq+Sx4uDpbPAfSNg83F/TtGY6r17Pb3E6r1SE7vwTZ+SU4lnTnty8QCBAR4o9JIwficGIydDodNBotFCo1vDxcMWlkLGrqGlBT14Dq2gbU3vr/nU4k+wexSIAB3f0R4OXSYhdHAFBrdajTyXEotQaH087A00WCUB8XhPk6w9tNBolIALFQAK1eD7VGB4VKi/zyeuSW1iOvvL7FwjFjsZE+uHdsL7hK7aM7YHWd6Tizo3VulEnYMcGqesec7FWl0aJeYdqJNczbsX6f9iLczwUrJ/bEgpEROJVWghNXi1BWY9nnnkwsxPAoH4zu40+dGgkhhBAAOp0OL77+LhMTCoXY8Pk7uHf+dJ6yIoQQQgixHLoimpAO4DgOb7/2LCYuetQQKyopw4dfb8Brzz3axp6d4/dDx7HrQLxJ3N3NFfctntupxT0SsRgLZ0/DwP59sW7jNhSXljHrD588g0alEssXzIBAQMU4nUkmk2HqxLGYOnEsampqkZWTh6ycPNzMzm2zo6MxjuMQEhRoKGj08fZiilV7du+G8NBgZOfmG2LxCUmI7d8XMpkMHu7u8HBvvXuoWq02FDrW1tYZihHFYjEkYjHEYhEkEgnEIhEkEjEaFSoknr+ME0nJqKiqafV2/6DVaqHVmg4AtkUgECC2b0+MHT4IPbuF2mVxbnUN+9g4YnFjWLA/buY0P+9y8gp5zMZ6jLsThQUH8JQJaUlokD+WL5iO++ZPQ2V1LXLyi5p+CoqQk1/casGjUChAkL8vwoIDEB4cgLDgAAT6+1BXTkIIIaQVf3vzAzQ2KgzLAoEAX3/4FhU2EkIIIYQQQogZ/Hy88OLqB/Cvdz83xHbuP4oTp89jdNwgHjNrUl5ZhY/XbEJpeaXJuoiwEKy8dwEC/X2tnodcLsO0iWMR268v1m3ahpvZucz6gpIK/O+H7XhmxWz4eLpZPR/SjOM4LJk5Hll5RcjMKUBWXhHyi8ug03W8OE8mlaBnRDCiIkPQPdQPMqkEACASCbEv/uytcUwR1GoNPN1cEDegN7O/TqdDXYMCNTU1qK69VfhYV4+aukZU19ajprYBSrUaYpEQErEIYlHT7YlFIvSIEGLUwF5Iz6tAel4FGpRaCIVCCAQCyGQyuLu7G8YtK+tUqKyrwKUs8zqmcQD6hHlhfEww+oTeGgfW2kcBXXW9glmWS8WQONjEkUEeMgDNY/u5pe13GrVHBeUNMH7ZykQcfFwl/CREAAAuMjEmxwZjYkwg0nKrcDm7Erll9cgvr4e6jY6OLeEA+HvIEOrjjF5BbhjU3RtSsdA6iRNCCCF2aP0vO3HhcioTe++fL1FhIyGEEEIchmOdtSOkE40fORTTJ4zC3iMnDbF3P1+LJ1YtgbeXB2957T+W2GJh47DBA3DPvJlW7dbYlm7hoXjlhSexa99hHDx6klmXePYSREIhls6bZpdFYo7Azc0VMf36IKZfHwBAdXUNsrJzkZXbVOxYVc0WwolFYnTvFoaonpHoGdkNLi6tzzTLcRwmjxuNNRs2GWINjY1ISDqPiWNHtpubWCyGj7cXfLy97ui+SCQSTB8/AlPHxuHKtUzEnzqP1Iysu+rG2BoPd1eMHjIAI4fGwMPOiwGNf6ce7o43mB4eHMgsGxcBOgqT4sYgKm60RRzHwcvDDV4eboiN7gWgaZZztVoDtUYDtVoDrU4HiVgEkUgEqURMRf+EEELIHTqXfBlrN25lYg+vvMchu3YTQgghhBBCSGd74YlV+Py7jSgtby6QeuU/HyJ+x1pex/XKK6vw/lc/oqq6lomLRCLMmTYRk8aN7PRzrP5+PnjxqYdxOD4RO/ceYro4llXW4IMffsMLD8ynAsdOxnEcuoUGolto07iRSqVGTkEJsvKKcDOvEIXFpe0WO3q6uyCqWyiiIkMQFuQHobCp4Eava+4kN6RfT5xOTkNVTZ0hdighGT3Cg5jnokAggJuLE9ycJAgxY0hHq1YiJasM8ZfzkJZrXgFjS5ykIozsE4hR0UHwdbfPrmlVRp0b3V0cr8ufcefC/Ip6aLU6CIWONcaUU1pnEgvzcaLrS2yEgOPQN8wTfcM8ATR1wy2qakROaT1yy+pQWaeCWquDWqODXt/UjVYsFMBZJkKItxPCfJ0R4u1ExYyEEEJIK+rrG/DaWx8zsT69IvHUQ0t5yogQYms4rumnq6PHgBD7RsWNhJjhzVeeYYoba2rr8PYn3+G911/gJZ8Tp8/jtz2HTeJzZ0zGtIljeD+x+0cXx+DAAKzftA06nc6w7vjpC5DLZJg/fTx/CRIDd3c3DOjfFwP69wUAVFVXIysnD/X1jfDx9kRkRBjEYvEd315oSBB69+yOtOs3DLHEpPMYOjDGap1UBAIBYvr0QEyfHlAoVcgrLDF0S8vNL0JRaUWbBY/OchlCb3VLCwvyR1hwALw93Xl/HVmCTqdDbR07a6e7m+MNpBt3MKyorEJdfT1cnFsvxrU3Op0OuUYdKcPMGQknnYrjOEgkYkgkd/5+SgghhBCWXq/HC397k4m5ujjj3399jp+ECCGEEEIIIcTBuLo447XnH8Nzf3vHEDuZdAG79h/DnGnjecmpuqYOH3+70aSw0dfbC088tLxTujW2RiAQYPL4UejfNwpffPcjSsrKDeuqaxvw8fqdeOHB+fBwdZyxCnsjkYjRIyIYPSKCAQDKxgbkFJbgZm4RsvKLUVjSNIYYEuCDqMimgkZfL492xwhFIiHGx8XgtwMJhlhJeRUuX8vCgD6RFr8fQoEAMZF+iIn0Q0lVAxKv5uNGQRVyy2qh1ujav4EWeLpIEerriphuPhjUwxcSkX0XGlXXsZ0b3V3ss0izLaFe7H3SaPUorGxEiI9jvcfklpl2pAzzdrzfp6MQCgUI9nZGsLczRsCv7Y31HXu/IoQQQrqSdz/7DgVFJUzsvX++BJGISgAIIYQQ4jjokw0hZhjYvzeWLZiOn7ftNcQ++34jnn14GcJCAtvY0/Ju5uTj59/2msTvXTAL40bFdWou7YkbPABymRTfrNsErVZriO8/loiQID8MienLY3akJR7u7ojt727WbUwaNwrXMjINBYVqjRrHTp7G7OmTLJFim2RSCXpEhKBHRIghplCqUFlV09QxTaOBVtvcMc1JLoOnu6tDFDK2pLa2jikuBgB3O+9E2RJ/X2+IxSKo1c0zI2fn5CO6Ty8es7KsopIyqNRqJkadGwkhhBDSlfy2ez/iE5KY2CvPPwl/Px+eMiKEEEIIIYQQx/P4qiX48Kv1yMrNN8ReffMjzJw8xtDBrrMolCp8+v0mlFZUMfGgQH888+gqmxnv8PfzwQtPPYxPv1mHvIIiQ7y8qhafbtiFlx5aAJlUwmOG5A9SqQQ9I0LQ89Y4olqtAcdxEHWgsK9/VAQSLqSipKzSEDty6iKie4Z36PbulJ+HE+aN7AkA0Op0KK5sQE5JDXJLa5FXVosGhRqaW13TBAIOYqEAYpEAXq4yhPm6INTXFWG+rnB1cqznZHW9UXGjs+N1bnSSCuHjIkZZXfN4YU5pncMVN7bcuZGKGwkhhBDi+PILi/HuZ98xsanjR2L6xNE8ZUQIIYQQYh1U3NgivopZWu8gZn3m3mcz9ucEHd/X3Bm8hNKO76tpOhH+7xcfwy87D0CjaSrSUypV+Od7n+O79//e9v7qho4fW8TmrVZrsO6XnSZd6BbNmWZzhY1/iInujYeWL8a36zczeW/avh+9IiPgZulufmY9V8x9bZrx+mijs6C98fXxxsD+0Th/6Yohdv7SFYwYNgjeXp6dno9MKkGgv4UveLaT31dVdQ2zLL5V0MlL/lYsIBUKBQgN8kdmdvPFFmeSLztUceOZC5eYZQ83F7hZc7ZnO3mOm7LTvHl9vM08tjmf78xh7mNmt0Xt9po3sSvmvL7M/rrJ03uK2cx4zMx+P+NtZ3Tm312VSoU/v/4OEwsPDcZzTzzYgVuz088LdquL/u1q6bOG3X7+IIQQQkiHcJzl//6b9f3BnFx4PA9j1l028zumvXYXEplRAKLVQCoS4N9/eRKrnvmbIZxyLQM//rIDq5bMaXN3vVDd5vq2cALTyxm27zuG/KJSJhYU6I/nn3wQzk5OHT6WNbi5uuC5Jx7Eh19+zxQ4FpZWYtuhJCybM8F0J3OfYwIzXiBalXnHNodZ72WWfV2KxR2/jEYgEGDyyFj8tOOIIVZdW48zl9MxYmAfdmNz349aeR8VCoUI8nFFkI8rhre2r7bjr8u2jn1n+5pxv4Xiu9q8ql7FHM/DzQVo4X3F6sz929/O+0Kot4wpbsworMHIPv7mHdOG1DWqUVjBXl8jEQkQHeENiFspGr7L54rF9tVp29/GWsz9+6HVtL9Na3Q8/u1RK9rfpjUCM4rOzX4PN+N6NbOObeZneLEZn7UEZry2AEBizgQWfH1nMheP4xZ03pp0Bnu9BsmsvO9+37+99SEaG5v/3gkEArz3+otN7053lYudPt7m4PUckJ2eczPruC083nZ7rQchhBA+0F8NQszUPSIEjy1fyMTW/bIbV9MzOy2HnQfiUVxazsSmThyDSeNGdVoOHTEwJhr3LZ7LxOobGrHxt70mhZrEMYwfPRwiUfOAkU6nw+H4BB4z6pqqa2qZZXc3x+1SGdOnJ7N8PvkK6urrecrGstQaDRJOn2NiA/o6TuEmIYQQQkh7Pv1mHW7czGZib//jz5DJzLgwhBBCCCGEEEJIi+5bMAMxfdlz7q+/+wWUys4riEvPzMaxRPa8uK+PF555dJXNFTb+wclJjqcfXQU/H28mfuLsFaTdyOEpK2JNPcKDEB7CFpYdP3MFik58rZAmNfWNzLIjdm4EgEhf9v3vwo1y1CvMLGC1IaeulZjUzg3r4QVpa4WNhBBCCCEO4tzFFKzdtJ2JPbJiEfoZXQ9HCCGEEOIIqLiREAv4+3MPwdmpecZTnU6Hv/3fF51y7Js5+Th0/DQTCw8NxpxpEzvl+OYaFTcYA2OimVhyyjWcu5TKU0bEmtzcXBE3OJaJpaSlI/+22WqJ9bVU3OioRg6JgUjUPLCl0WqRkHSex4wsJ/nyVdTWsYWaY+Ji+UmGEEIIIaSTlZVX4N/vfsLEhg+Jxb0LZvGUESGEEEIIIYQ4NoFAgLdeeYaJZecV4ot1v3TK8RVKFdZv+Z2JicVirH54hc2Pc7i5uuCpR1ZCImY7Fv244zAVvDkgjuMweeRAJtaoUCLh/FWeMuqadDodaurZjm7uLo5Z3Dg4wg3C267+Umt1OHWttPUd7IhOr8fxFNNrCSb0c5zOlIQQQgghLdHr9XjxH/9lYq4uzvjXn1fzlBEhhBBCiHVRcSMhFuDv643nH72Pif227xgSz12y6nFVajXW/bKT6XIoEgqx8t4FEArtZ5a6pQtmwcWZnU1w0/a9qKmt4ykjYk2jhw+FTMp2Ujl47AR16+xEVdU1zLKHuxtPmVifi7MTBvXvzcSOJ56FTqfjKSPLiU9IYpZ7dgtFkL8vT9kQQgghhHSuf7/7icmkHR/85zWH7UhOCCGEEEIIIbZgxsTRGDt8EBN766NvO2VMb/u+oyivrGJi82ZOhr+vj9WPbQm+Pl6YP2sqE6uoqsW2/Sd5yohYU0iAD3p3D2Vip5LTUFvXwFNGXU9tgxI6o3Z/Hi7yVra2b25yEWLD2PHeEylF0DnA+HtqbhXKa5VMLMzHCf4ejlmoSgghhBDyh99+P4T4xLNM7JU/PQJ/X2+eMiKE2DKO4+jn1g8hxH5RcSMhFvLSE8vh7enOxF556zOrFmzFJ55HcWkFE5s5dQKCAvysdkxrcHV1wb0LZjOx+oZG7D50nKeMiDXJ5TKMGTGMid3MzkVmVg5PGXU9XalzIwCMM7rQoryiEldS03nKxjJy8wtx4yb7mhkbN7CVrQkhhBBCHEta+g18vmYDE1u6cDaGD6XPQ4QQQgghhBBiTRzH4e1Xn2ViZRVVeO+LdVY9bkZWLo4lnmNiPbqFY/yoOKse19LGjhyKnt0jmNiJs1eQfjOPn4SIVU0aEctcVKdWa3DszGUeM+paqusamWWRUAgnmYSnbKxvbJQns1xao8C1vGqesrGclro2Tomhro2EEEIIcWxKpQp/+dd7TCw8JAjPPbqCp4wIIYQQQqyPihsJsRA3Vxe8+uyDTCz+9AXsOZxglePpdHocO8UO4oWHBmPK+FFWOZ61DRoQjYH9+zKxpPOX0ahQtrIHsWfDBg+AmytbUHfgyHHq3thJulpxY0RoEEKD2EGuX7bvgUJpn+8vWq0WP2/ZwcTcXJ0xoG8vnjIihBBCCOlcf379bWi1WsOyVCrB2//4M48ZEUIIIYQQQkjXMWLIAMybNp6J/e/rDSguLbfaMfcdTWSWxWIxVtw7HwKBfV3uIBAIsPKe+ZCIxUx8//FzrexB7JmPlzsGRvdgYhdSbqC8soanjLqW6noFs+zuInPoDg6RvnIEeUiZ2L7zeXbdvTG3rA4p2ZVMzEUmQkyEZyt7EEIIIYQ4hs+++wk3snKZ2Dt/ew4ymbSVPQghhBBC7J99ne0nxMY9uXIRwoIDmNir73wGnU5n8WOlXs9EeUUVE1s0dzqEQqHFj9UZOI7DonkzmAEFpUqNpAs0e6UjEovFGD96OBMrKinFlavXeMqo69Dr9aiqZgdN7bXI705xHIcJI4cwsfKKSmzffYCnjMxz6FgCsnLzmdjYYQMhEtnn+z8hhBBCyN04ePQEdu07zMReWP0wwkODecqIEEIIIYQQQrqeN195miksrG9oxH8+/MYqxyqrqMTV9BtMbM60ifDz8bbK8azNx9sLc2dOZmKpN3JQUl7FT0LEqsYN68+M3+h0Ohw+dZHHjLqOqlq2c6O7i5ynTDoHx3EYY9S9MaOwBievFvOUkXk0Wh02HMmAcWnmA+O7QShw3CJVQgghhJCy8kq88f6XTGz44AG4Z940njIihBBCCOkcVNxIiAXJZFL866XHmNil1Az8/Nt+ix/rWCI7g2dwoD+6R4RZ/DidycvDHTHRvZlYfOI56ubnoGL794WvNzvwfPh4AtOBhViHTs8WXO85eBTHTp5y6NfasIH90CMihIkdS0hCesZNnjLqmMKiEpOL+f18vDB57DCeMiKEEEII6TxarRYv/v0tJubn642//ukJnjIihBBCCCGEkK6pb6/uWLVkDhP7esOvyMzOs/ixjp9Oxu3DF3KZDGNGDLX4cTrT6OFD4CRnC62On6EJXx2Rm4sT4gaw499Xr2cjv6iMp4y6jtoGtnNjfmkVrueW8pRN54iLdIens4iJbUvMQlmNopU9bNe+83nIL29gYhF+zugd4sZTRoQQQgghnePf73+B6ppaJvbBGy87dBdyQgghhBCAihsJsbgVC2cgOiqSif3jvS+hUqktdozyiiqkXMtgYmNHDnOILzDjRrIFOoUlZbh+M4enbIg1CQQCTBo3kolVVlXjXDIN3loTx3Ho3zfKJH7keCK2bP8darXl3qtsiUDAYcWiWRCL2QG99Zt/s5vOlVqtFus3bYPmtgJgjgNWLpoBiVjMY2aEEEIIIZ3jh5+24FJKGhN745UX4ObmylNGhBBCCCGEENJ1/fPFxyGVSgzLarUG//jv5xY9hlqtQcJZtsvd8KGxzHHtkUQsxohhA5nYqeRUi44nE9sxekg05DIpEzuYkOzQk47aAlcnGbOsVGnw84FzSLh802Efe4lIgOUjgpiYSqPDT0czoLOj+5xbVod9F/KZmFDAYfW0HjxlRAghhBDSOdKuZ+KL7zcysWULZ2L4kAE8ZUQIIYQQ0nmouJEQCxMKhXjz5dVM7GZOAb7asNVixzhxhp2hVCaVYuigGIvdPp969egGP1+2m9/xU+d5yoZYW1TP7ggNZgdYjp08DaWdFJvZq9nTJmFgTLRJPCUtHd9t2Izq6hoesrI+Px9PzJ82nomVV1Riw+bt0Ol0Le9kI/R6Pbbt3o+sXHYgb+KooegeHtLKXoQQQgghjqO2tg6vvfk+E+vfNwoPr7yHp4wIIYQQQgghpGsLCwnE6gfY72Q/bduD5CvXLHaM85dTUd/QyMTGjhjWytb2xbj7ZEOjEudSrvOUDbEmmVSC0UPYcbmsvCLcyCnkKaOuIS46HIE+7kxMrwcOJF3D9uNXoNFoW9nTvvUOdMaonh5MLL2gBsdTivhJ6C6pNTpsOJIBnY4txlw6KgxOUlErexFCCCGEOIa//Os9aG+b9F4qleCt157nMSNCiL3gOPr544cQYr+ouJEQK5gzZQxGDWVnS/nPR9+htq7eIrd/NT2TWY4bEguZVNrK1vZFIBBgrNFg3tX0TIedPbGr4zgOk8eNYmL1DQ1IPEMFrdYkEokwd8YUTJs41qTja2FxCb5ZtxF5+Y45oDpuxGD0iGCLAc9fvIKNW3fZ9PvM3kPHcDg+kYn5+3hhzpQxPGVECCGEENK5/u/jr1BcUsbE3n/jVQiFQp4yIoQQQgghhBDy6jMPw83VhY29/bHFbv/0hSvMclTPSPj7+Vjs9vnk5+ONvlFsF7LTyWk8ZUOsbVhMFNxcnJgYdW+0LolYhAdnD0d0ZKDJuovX87F27xnUNTjmhLvzB/nB05ktBPw1IQuXsyt4yujOaHV6fH8wHfnlDUw80t8ZI6Ic472fEEIIIaQ1B48lYtf+Y0zs+SfuR3hoUCt7EEIIIYQ4FipuJMQKOI7D2688xcRKyyvxwdc/mX3bao0WBUUlTCy2fx+zb9eWxPbvyyw3KhQor6jiJxlideFhIejVPZKJJZw+j/r6hlb2IJbAcRxGDBuM5UvmQyqRMOvq6uvx/U+/4OLlqzxlZz0CAYcVi2aZ3OcTp85i07bdNtfBUa/XY++heOzce5iJCwQcVi6eCYlYzFNmhBBCCCGdJycvH+9/9i0TmzllPKZMoIkeCCGEEEIIIYRP3l4e+PPq+5nY3iMJOJpw1uzb1un0yMotYGKj44aYfbu2ZNRw9v5k5xfb3DgFsQyRSIjxw9nJkYvLKnElPZunjLoGsUiIRRNiMWFwL5N1ecVV+HZnIgrLqnnIzLrkEiGWj2Avgtfp9PhufzrS823z/up0evx4NAOXstgCTCeJEE/P6MlTVoQQQgghnUOr1eKl1//LxPx8vPHKnx7lKSNCCCGEkM4nan8TYj/M6KVrdhtec27AjH3NncjQnP7DgrYLSkYPH4rZk8di18F4Q+z9r37E6geXwtfbq8OHLSypgFbLDmqFBTvW7CyeHu5wcXZC3W3FbTn5RfDx9jTjVs14svD5PNPb6wDm3d3nyeNH4XrmTcPspCq1CvEJpzFjygRrJEdu0yMyAo/evww//7oD5RWVhrhWq8W23ftQXFqGyeNHQyCw0nwIPMxI6+ftgcdXLMDna7dAo9Ua4vEJSWhUKLDq3gU20QFIr9fjt90HcODoCZN1KxfNQmRYMA9Z8YnH2YvtduZkO83bbh9vQjqBOZ8reWWvecO89ySz3s/MfC8069hm/r7MeJ7qW/n+8+q/34VC0TyTvlAoxLv/+qvR9nw+ZoQQQgghhNwhvd70syef3/XMGr8w9+Bm3ACfeXM8zR1sw+NFzz26HJ99vwlFJWWG2CtvfYyEnWvBcVyHcy8tr4RCqWJiPSLDzcrV1vToxt4flVqDoopaBPl1fCwZem3727SGM3NsRGDGa1Ot6fi+Zn+nN+P1dRfHHhAVgcTzV1FaUW3Y98ipZPTtHtz541LmjvvpzPhdm3NfdXe/Lwdg7OAo+Hq547cj56BSN79GqusU+H53EuaP7Y++3QLauSEe547X3f3rure/DJP6eOJQavP4r1qrwxe/p+KRqVGIDjfn2gvL0mh1WHf4Os7fKGfiAg54ZlZUxyZ7Nef9jM+/ueYcW2vG+7+5xxaZMyGvpP1NrMWc17VYauaxzblezYznt9l/6zt+qSknNHPiZjOOzes4lTmfU8xJ29zPR2Y9ZHY8Lsgbs37ZZh6bz/FM2/2e2zZzxo9bvs/f/7QVl66mM7E3/vo0XJ3lzfuY+7rmdSyUz+c4uSt2+7okhBDiCKhzIyFW9OYrTzcN2t1SV9+ANz9aY9ZtZhewXRv9fLwhl8vMuk1bw3EcwkLYgs3s/EKesiGdwc/XBwP6sR1IzyZfRmWVbc4c6Wh8vL3wyMqliIwIM1mXkHQOP2/ZAaVS2cKe9qt3jwg8tGwu8x4NAGfOX8J7n36LQqMOuZ2torIKn36zrsXCxnvmTEbcwGgesiKEEEII6XxJ5y7ixy07mNjjDyxDn6gePGVECCGEEEIIIeR2zk5y/P15tpvE6fOX8dveI2bdbo7RmKi7myvc3VzNuk1b4+riDE8PdyaWW1jKUzbE2gQCASaNiGVildV1OHslg5+Eupg+3QLw0Ozh8HCVM3G1RotfDifj6PkMw0S8jmLeQB/EdWeLGNVaHb7al4bEtGKbuL91CjW+3ptmUtjIccBTM3oh1MeZp8wIIYQQQjpHbV09/v7OJ0ysf5+eeOi+BTxlRAghhBDCDypuJMSK+vfpiZWLZzGxL9f9gqzcgg7fpvGAlnERoKMwvl85VNzo8CaMHgGRqHmmNa1Wi8PxCTxm1LXI5TKsuGcB4gbHmqy7nnkT367bhIrKqk7Py5pio6Pw8LJ5EArZj0PZufl4+39fYN/heGjNneHyLun1epw8fQ7/ee8zpKbfMFm/eNYkjB8xuFNzIoQQQgjhi16vxwt/e5OJubu54vW/PMNTRoQQQgghhBBCWvLIfQvQPSKUib329qfQaDrejc+4uDE0OLDDt2XLTMZEC6i40ZH16haM0EBfJnb8bAqUKjVPGXUt/l6ueGTOcIQFmHYtPHYhA78cTobKnC6iNkbAcbhvVAhiw9kiap1Ojx+P3sDX+9JQXa9qZW/ru3SzAm9uSsbV3ComznHAY1N6ICrYveUdCSGEEEIcyDsfr0FxKTvRw3v/eqnzu7sTQuwbx9HPHz+EELtFxY2EWNk/X3oSEonYsKxSqfH6u593+PZyC8uY5dCQrjGQl5tfZBMzBxLrcXd3w9CBMUzs8tU03jvodSUCgQAzpkzA3BlTTE6QlJaX45u1P+Nmdi5P2VnHoP698cTKxRCLRUxco9Vi++8H8d6n36Kgk56DFVXV+Ozb9fjxl+1QGHXK5DgOqxbPxMRRQzolF0IIIYQQW7Blxx4kJJ1nYq+9+BR8fbx5yogQQgghhBBCSEvEYjHe+MtqJpaWcRNrN+/s8G3mGhU3hocGd/i2bFmY0VhvDnVudGgcx2HyyFgmVt+gQOKFNH4S6oKc5VKsmj4UA6NCTNalZhXju92nUV3XyENm1iEUcHhwXBiGdfcwWXc5qxJvbk5GUnppp16LUadQ44eD6fh6XxpqG9nCXqGAw+op3dE/3LQAlRBCCCHE0WTnFuCDL9cysVmTx2LKuJE8ZUQIIYQQwh8qbiTEyiJCg/DEqiVMbMOvv+PS1fQO3V51XQOzHOjv1+HcbFlgAHu/GhoV0Gg6t4Ma6XxjRgyDVCJhYgePnuApm65r0IB+WHXvQjjJ5Uy8UaHA+k1bceb8RZ4ys47oqEg8/+hy+Pt6mazLzs3Hm+9/hq9++Blp6Teg0+ksemy9Xo+b2bn44edf8c93PsLVaxkm27i7ueCpB5Zg+KD+Fj02IYQQQogtUyiU+Ou/3mVikRGheObRlTxlRAghhBBCCCGkLffMnYqB/XozsX++/yUaGxUdur2CErZzhcN2bgxmJ3wtLKngKRPSWcKC/NCrG1usm3ghFXX1jlNQZ+uEQgHmjIrG9OF9TJo6FJfX4psdicgtruQnOSsQCjisGB2K8X18TNY1KDVYd/g6vtqbhryyeqvmodbocOpaCd7clIyzGWUm6+USIf40syf6hFDHRkIIIYR0Da+99RGUyuZO2kKhEP99/UUeMyKEEEII4Q8VNxLSCV7708NwdXE2LOv1erz21scdui21WsMsGxeCOYqW7pdao2lhS+JInJzkGD18KBO7kZWNzKwcnjLqusLDQvDYA8vg78sOcul0Ouzefxi79h6CVus4BccRoYF45ZkHMXnMMJNBTL1ej4tXUvHx12vx73c/weH4BDQ0mDfArFSqcPL0Obzz4Zd495NvkHTuIjQtvMcNH9QPf//Tw+jbs5tZxyOEEEIIsTcff73WpGv4/73+MqRSKU8ZEUIIIYQQQghpi0AgwNuvPsvE8gtL8Nl3P3Xo9m6/wBMAPNzdOpybLXNzc2WWFSp1p3ZQI/yYNCIW3G0DUiq1BvFnU3jMqOvhOA5x0eFYPm0IZBIRs66+UYW1e84g+Xo+T9lZnoDjsDguCI9MCIerTGSy/kp2Jd7ZchEfbLuMM+mlUGstN+FrWY0C209l4+8bzmLDkQyTbo0AEB3ihtcW9kGkn4vFjksIIYQQYstOn7uEn7b+zsQev38J+vSK5CkjQgghhBB+mZ6xIoRYnK+3F158YiX++d6Xhtjug/E4fuocxgwffFe3pTbqXigWiy2So60RiUzfntRqNSCX8ZAN6UxxQ2KRdP4iauvqDLGDR0/g0fuXMYN8xPo83N3x8Mp7sXXnXqRdv8GsO5t8CWUVFbhn/mw4OclbuQX7IhGLsXDmRMRGR2Hdlt0oKTOdHbmktBxbduzF1l37ERTgh7CQoFs/wQgO9GvxPVmr1aKwuBTZufnIyStAbl4B8gqLWyxm/IO7mwuWL5iOflHdLXofCSGEEELsQUlpOd764HMmNnr4ECycM42njAghhBBCCCGE3Ikp44Zj4uhhOHwiyRB7+8Ov8ciKxXdVnKjT6aExKqwRtzB26AgkRuMKer0eOp0OQqGQp4xIZ/Dz9kBM7264mJppiJ1PycDwAVHw8nBtY09iad2DffDI3BH4+cA5lFc3GOJarQ7b4y+juKIWU4b2gsBBhqljw93Rw98Zv5zOx7mb1SbrM4trkVlci18TbmJEb3/0DnFHqK8LnKR3/h6s0+tRVq1ATmkdzmaUISW7Eq2VbMvEAiwZHophPbzoWgBCCCGEdBl6vR4vvv4uE3N3c8U/X1rNU0aEEEIIIfxzzBEAQmzQC4+vxGffb0JpeaUh9sqbH+H4jrV3eZK2a8zU2dJD0jXuOZFIJBg3Kg679h0yxAqKinE17Tqi+/TiMbOuSSKR4N6Fc3DkeCLiE04z67Jy8vDNup+xbNFc+Bl1eLRnkeHBePXZB/H7oZM4lngOSpXp7KE6nQ55BUXIKyhCQtJ5AE2zUjs7ySEWiSASiaDRaqHRaNDQ0AjNHXa5FAmFGD6oH+ZPHw8nKuYmhBBCSBf1z//7CDW1dUzs/TdepQucCCGEEEIIIcTGcRyHt199FnEzVxhilVXV+O8na/DW356/49vRtnBOXShyzGI/UQv3S63RUnFjFzAhrj9SrmdDc2tyY61WhyOnL2PRtJE8Z9b1eLs745E5I7Dl6EXcyCtj1p26koXSqjosHtcfMqljTDztIhPhwXHhGBhRjU2J+ahVmE7IWqfQ4EByPg4kN3Wv9HGTIczXGaE+LvBxk0IsEkAsEkKn00Ot1UGh0iCvvAG5pXXILauHQtX+2Gh0qBuWjQqDh5PE4veREEIIIcSW/bJjHxLOJDOxv73wOHy8PflJiBBCCCHEBlBxIyGdxMXZCX977lH86e//NcQSziRj5/6jmDttwh3fjlgkYro3ajSmRTeOQK02PYEucdAZWYmpgTHRSDxzHuUVzcXAh+JPonev7jSYywOO4zBx7Ej4+Xjjt9/3Mx0HK6uq8e26TVg0dzqiejpOl0GJWIz508dj2vgRSLpwBfGnLqCwpKzNfXQ6HWrr6jt0PC8PN4yNG4gRg2Pg6uLUodsghBBCCHEEKWnp+HrtRia2Ysk8DB0Uw1NGhBBCCCGEEELuxtDYaCyaNQm/7m6exPKjr9fh6UeWIyjA745uQygUmMS0Rp0cHUVLkyPSWFjX4O7qjKH9eyHxQqohdiU9CyMH9kagnxePmXVNMqkY900ZhANn0nHqShaz7kZeGdbsOo2lkwfC292ZnwSt4I8ujsdSy3AyvQI1jabXaPyhrEaBshoFzt8oN/u4vQJdMa6vL2LC3GkyM0IIIYR0OQqFEn9940Mm1j0iFE8/tIyfhAghDoHjWm4q1NXQY0CIfTMdFSCEWM3jKxejW1gwE3vtrY9bnH20NWKj2Ttb6ijmCFQt3C+x2DFmQiTtEwqFmDR2FBOrqKzChUspPGVEAKBf3yg8tPweuLq4MHGVWoWNW3fiROIZ6PWO1WNVLpNi3IjB+NtzD+O5R+/DoP69IRBY5uMTxwHRUZF4ctVi/PulxzF13HAqbCSEEEJIl1Zf34D7V/8ZOl3zBatyuQxv/v1FHrMihBBCCCGEEHK3/vPy00yBXmOjAv9+7/M73l8gEJgUON4+8aIjMZ7wlQMgaqG4kzim0YP7QiZlu9YdTLzIUzZEIBBgWlxvzB3Tz+Q9qKy6Hmt2nUZmgfnFfbbERSbCrIEBeGNJHzw8Pgy9AqxTvCkTCzC+ry/+trAPnp3REwPCPaiwkRBCCCFd0ruff4+s3Hwm9n//eAFSKXWzJoQQQkjXRm3QCOlEEokY//7Laqx8+jVDLOXaDXy/8Tc8snzRHd2Gq4sTqusaDMvFJaWI7t3T4rnyraiklFmWSSUQiWiW0q6kT1QPBAcGIL+wyBA7euIUYqJ7QyKhL/N8CQr0x2P3L8PGrTuZ341er8fBYydQUlaOuTMmQ+RgnVY5jkOvyDD0igyDQqlETn4xcvOLkJNfhJyCIhSXVrR7G96e7ggLDmj6CfJHWHAAnJ3knZA9IYQQQojt02q1WPH4Czh/kZ3Q5MWnHkFocBBPWRFCCCGEEEII6YioHhF4cOk8fPvjVkNszYYtePHJB9Cze8Qd3YZELEajVmlYrq2rt3SaNqG+voFZFotFVPDThTjJpRg5qA8O31bQmJlTiMzcIkSGBvCYWdc2sFcIvN2csfnwBdQ3qgzxRqUaP+47h6nDojCsb5hDvVaFAg4DIzwwMMIDhVUKJF6vQEZRPQoqFdDoOja5ratMhDAfJ/QP88DQ7p6Qiul6D0IIIYR0bTv3HcU//8tO/DNm+CAsmDmJp4wIIYQQQmyHY1UeEGIHls2fjvc+X4uLV9MNsZff+B9mTxmHAD+fdvcPCfBBXlGZYTknr9AqefItJ6+AWQ4NCnCowQHSPo7jMHn8aKz9eYshVldfj1NnLmDsqDgeMyOuri54cPkS7NhzEJdSUpl1l1JSUV5RiaUL58DV1aWVW7BvMqnUUOj4h0aFEkUl5VCpVVCpNdBotBAJhRCLRZCIxfDz8YKL822FjA7W4ZIQQgghxFwv//MdbN9zkIn1iAzHX555lKeMCCGEEEII+X/27jssinNtA/g9W4Clg9KLiAIKKvaCLfYWY4nR9MQkpvf25aTHnOTknFTTu6nGFo1dozFWULErWBBEFlCa9Lrt+8O4OrugyACzu9y/69ormWdmdp7FrfPO8z5EJMVrTz+AX35fg5qaCwWKBoMBT7z4NtYs/KpRY34B7b2RmZ1nXtbm5CI2pnOL5SuXrBzxmGhAe295EiHZDIyPQfLhkyivrDbHNiUexJyZ4zg+LqPwQB/cN3kQFv61H3lF5ea40WTC+t3HkV9SgYkDu1p1eHQEQd4umN7vwmRjepMCZ0tqkFVYhazCKuScr0JVrQE6gwk6gxEKQYCTSoBKqUA7dyeEtXdFeDtXhLV3hberms9hIiIion8cOJyKWx98HqbLrhkTBAHvv/E8vzMRERERgcWNNsaev6BKyV3CvlK/1EspLlE0bVY5hUKJd155ChNuecgcKy4pw+Mv/geLv33/qvuHB/lh18Hj5mXLIkBHYfm4wkOkzkwp4bmikDogIeHYJqO0Q8tVQNVMb2cdO4Sic8cOOHX6jDm2c/de9O3VHa4t1vHOTt+LpbwfNuF5olKpMO36cQjwa49NW3eITrzknD2Hr3/8DbfceAOCgwKanldLk/L6sPh7a1yc0THcDjoKyfm5CYnvRywIbWV2+l4oVTO+L5Ajk/pvzfezayfn60uufy9pj9kk6XeEtMcs4Np/P331wwK8/+m3opiPtxdWLfgG7u5ukvJpFbJ+xyIiIiIiIrJNIUH+ePzeW/C/z34wx9Zv3o5fl67C7TfdcNX9w4P9RcWNjjrhq9bicYUH+cuUCclFrVZheP/uWP33HnPsbP55pJ7SIi4q/Ap7Ukvz9tDgnkkDsHzbERzPPCdat/9ENgpLKjFzZDzcNM4yZdjyVEoFwtq5IqydKwbHyJ0NERERkX3KOZuHybc9hMqqalH89ecfRt+ecTJlRURERGRbWNxIJINxIwbj5qnjsfCP9ebY0tUb8ce6zZg6YeQV9w0PFnd3zCsoRE1tLVycHeuEuTbHYiAvNEimTEhuo68bIipurK2rw/akPRg3ariMWRFwYfaowQP7wq+9L5auWIc6XZ15XXlFBb7/dTGmThyLbrEc6SIiIiKi+v25eTseee41UUylUuH3Hz9DTFSkTFkREREREdFVSZ7kQ6ad7TZviRNxSpkEp4kTvgLAv56Yg5+XrsHZvAJz7MmX3sbY4YPg79fuivuGBfmJltvOhK8BgELCZRxS/q2VTk3fF5D2+jLUNnlXk67p+164A5kmibpssqNeXTsi6cAxFJVc6hC4OekgunQMhlLZwGtQymRJUidakvKeJOX5rWz9iTSdlGrMHNMPW5JTse1gumhdVl4xvl21GzeP7oUAXw9puV2JhPdhyZMpK6RMIi3h31rKYwakvR8JEo8thVEvYWeDtGOr1E3fV9L3FIl5y0XydzMJ72dKCdeHSX1+S5ooVsbXtaT3Qom/H6Q+V5pMxsmnJc9Z2vQ7kLX7nZ1OhmyScwJOuV7XRomfPdeYd0VFJW647SHknssXxe+YcT1efvL+1psEVc7nqKTHKOfrWsJ7uJyfPZI+AzgpLxERyUeuX09Ebd5Hb/4ffH28RLFHXngLJaVlV9wvJKA9FJedQDaZTMjOcayZSktKy1BaVi6KSe/cSPYqMMAPPeK6iGJ79h+66muFWk9050jcd+cs+HiL39P0ej2WrlyLzdsS5T0ZRkREREQ26WjqCdw0+xEYDOJBxK8/fAvXDRkoU1ZERERERETUXLw8PfDZf18Rxc4Xl+LJl/9z1X3Dg8UdDIvOF6OyqqpZ85NbdXUN8guLRDHLx01tg0KhwMiBPUSx86UVOHAsQ6aM6HKCIGBE786YMSIeapW4OKekohrfr96N42fyGtibiIiIiNoqg8GA2x56HgeOHBPFhw3sg6/ff13eglwiciiCIPD2z42I7BeLG4lk4u/XDh+9+X+i2Nm8Ajz/5odX3M9JrUKQv7h74+GUE82en5yOpIofj4uzE/za+cqUDdmCEUMHQam89JFlMBjx9/YkGTMiS/5+7ZXw/GQAAQAASURBVDHnzlsQER5qtW5b4m78uvgP7Nl3EOmnz6CktBRGo8RZQomIiIjIruXlF+D6W+5DWXmFKP6vpx7C3bfeKFNWRERERERE1NymThyFm24YJ4otXL4Oq//ccsX9gvx8oLIoIjp9Jru505PV6Szx41EqFQjyv3JHS3JcXTuFIdji33/rnqOoq9PJlBFZiusYiNkT+8PT1UUUr9MbsOivg9h2MJ0TvhIRERGR2bOvv4tVG7aIYlGRHfD79x/C2dlJnqSIiIiIbBSLG4lkdNuNkzBh5BBR7NtffseWnclX3C+mU7hoOSl5P+p0jjGoYTKZsC1xjygW0ylC1K2S2h4fby/07SmerfRwynHk5RfIlBHVx9VVgztmTUe/XvFW606dzsTajX/j50XL8NEX3+Ot9z/FZ9/8hIW/r8TGv7dj/6GjOJOVjYqKSg76ERERETm46uoaTLntAZzR5ojiM6dOxJsvPiVTVkRERERERNRSPn77Rfh4e4piDz//ptWEN5dTKpUICxJ3MUxKPtAi+clll8XjCQn0s+oKR22HIAgYM1g8xlZRVYNdh07KlBHVJ6i9J+bcMBAhfl5W6/7efwr/+fkvfLE8EYs3H8Rfe0/iYFoOtHnFqKqp4xgoERERURvy+fe/Yd5XP4tivj5eWP3Lp2jn6y1PUkREREQ2TCV3AkRtmSAI+Py/L6Pb8GmorKo2x+9/9g0c/GsJXF019e43pG8PbN65z7xcWVWN/YdSMLBvz5ZOucVlZGYh52yeKDZ0YG+ZsiFbMiyhPw4cTjEX8ppMJvy1NRG33jRF5szockqlEpPGjYS/Xzus27SlwQ6NBoMBBUVFKCgqslrn7OQEXx9vtPP1QTtfb7Tz8TH/v4uLSz33RkRERET2wmg04q6Hn8XufQdF8YF9e2H+p/+DQsF5uIiIiIiIiBxNgH97vD/3edzz+MvmWHbuObzw5gf4/H+vNrhf3x5dcFp71rx86OgxlJSWwdvLs8F97EVpWTkOHEkVxfr16CpTNmQrIkIC0LlDEE6dufS8TzxwDH27dYarxlnGzOhy7q7OuHtCP6xKTMXhU7midTq9AfnF5cgvLr8UFC5M5KxxVsPX0w2+nq5o5+V24fbP/zupefkWERERkaNYv3k7Hn/xbVFMrVZh2fcfIiqyg0xZEREREdk2nh0jklmHsGC8/eITeOLld8yxU6ez8Pp7X+B/rz5d7z4Bfr7o0ikcx9OzzLFtiXscorhxq0XXRr92PujSuaNM2ZAtcXNzxeCBffD39l3m2Mn008jMykZEeKiMmVF9+vWOR/t2vli8fDWqa2quad/aujqczcvH2bx8q3Vurq5o5+sDXx8vUdGjr4831Gp1c6VPRERERC3k5bfex5IVa0WxiPBQ/PHLl9BoOJEFERERERGRo7pr1lT89vtabNyaaI59+cMi3DxtIoYN6lvvPgN6xWHFn9tRp9MDuDBhzo5de3H9uJGtknNLStyzDwaDwbysVqswsHecjBmRrRg1MB7pWefMXf5q63TYvi8F44ZwQmBbolIpMXVoNwT4uGPT3pNoTFPG6lodcgpKkFNQYrXOw9XFXPRoLn70dIOPhwYqNScDIyIiIrIXR1JPYtZ9z1g1BPjmg7kN/vYlIiIiIhY3EtmEh2fPwsI/1iFp7yFz7IMvf8KsKePRJz623n2GDugpKm7MzMpGVnYuwkODWzzfllJWXoEDh8UzlA4d0BsKhSBTRmRrBvXrjaQ9B1BTW2uOHUk5zuJGG9WxQxgenXMXDh5Jxbn8AhSdL0bR+WLU1tU1+T4rq6pQWVWFrOwcq3Venh5o5+uD9r6+6NOzGwL8/aSkT0RERETNbP6vS/CfD78QxTw93LFm4XcI8PeDyVR/128iIiIiIiKyf4Ig4Mv3XkP34VNRVVVtjt//9Gs4+PcyuLhYd6XTuDijX3wsdu49bI7t3L0PE0YPh1KpbJW8W4LBYMD2pL2iWN/uXeCqcQEMTR9DIccQ6OeDblEdcORkpjmWfDgNw/rGQVPP64TkIwgCErp3hJ+3OzbsPo6isqom31d5VQ3Kq2pw5tx5q2N4u7vC92Knx4vFj55u8PbQQBB4LQURERGRrTibV4Drb3sI5RWVovjLTz+IO2dNAYyGBvYkIpJGgMDfh7jwdyAi+8XiRiIboFQq8c37r6P3mJmoq9MBuDDz6H1Pv4Y96xfU242se0wneHu6o6Sswhz7fdV6PPHA3VAo7HPmvhVrN4pnKFWpMLBPdxkzIluTey5fVNgIoN7BbrIdFzpuXpp1ymQyoaqq+kKhY3Exis6X/FP0WILzJSXQ6/VNPlZpWTlKy8qRkZmF5AOHMHn8aPSO79YcD4OIiIiIJPp7exLuf+olUUypVGLpD58htkuUTFkRERERERFRa+rYIRT//tfjePqV/5pjJ9MzMff9L/D2S0/Wu8/QAfGi4sbSsnLsPXgEA/r0bOFsW87+wykoKS0TxYYN6ClPMmSTIkL8RcWNBqMRhcVlCAvixJ62KCrMD51D26O8qhbny6pQVFaJotIL/z1fWoXiihoYjE2b1MtkMqG4vArF5VVIzy4QrQto54nJQ7ohxM+7GR4FEREREUlRVVWNKXc8Am3OOVH85mkT8Mb/PSpTVkRERET2g8WNRDYiNqYTXnryfrz2v8/MsUMpJ/DeFz/iX4/fZ7W9UqnAkH7xWP3XTnMsLT0T25OSMXzwgFbJuTmlHDuJpOQDolif+Fi4u7nKlBHZGr1ej1XrN4liTmo1+veOlykjagpBEODm5go3N1eEh4WI1plMJpSVlaOouMTc5bHofAmKiotRUloG4zUM+plMJqxctxF1dXUY2K93cz8MIiIiIroGx0+mY/qdD1lNZPH5u3MxZsRQmbIiIiIiIiIiOTx2321Y9Mc67N53qWDx3U+/x8wbxqFn965W24cF+SMyPBgZWbnm2O8r1yM2ujM8PNxbJefmVFFZiaUr1oliHUIDER4SIFNGZGuqa2rx9+7DopibxhlBfr4yZUSNIQgCPN1c4Onmgogg8b+VEQJKKqpRVFqF86LCx0qUVtbAZDI16Zh5RWX4ae0e3DK2DyKC2jXHwyAiIiKiJjAajbjzkRew92CKKD6ob098P+8tdlMjIiIiagQWNxLZkP979B4sWbkBR4+fMsfmvv8lpk8chZjOHa22H5HQGzv3HkZxabk59seajYjrEoX27exncKOquhq/Ll0pirk4O2HSaF7kSpdsS9yDovMlotio4Qnw8vKUJyFqdoIgwMvLE15enoiMCBetMxgMKCktsyp6LDpfgrLy8gbuEVj/11bU1ekwNKE/TxQRERERyaCw6Dwm3XyvVUeKZx+dg/vvvkWmrIiIiIiIiEguSqUS33wwF31Gz4BOd2ESHIPBgPueehW71v8Glcr6EoZRQ/oiY8GlscSKyiosWr4G9905q9Xybi6Ll69FeUWlKDZ6SF+ZsiFbtCnpECqqakSx8UP7QKVSypQRSaVQKODr6QZfTzcA4u6ber0B58urUFRa+U/x46XCx4rq2qved51Oj1/X78XM0b0QFebfQo+AiIiIiK7kX//+EMvWiJs2dAwPxR8/fQIXF2eZsiIiIiKyLyxuJLIhTk5qfPvBG0i4/g5zh7La2jrMeeZ1bFk+HwqFQrS9i7MTbps2Fp/+8Ls5VltXh58X/4EnHrjbantb9fuqDVYXuk6fOArtfLxkyohsTX5BIXbs2iuKhQYHoh+7NrYZSqUS7Xx90M7Xx2qdTqfD+eISFJ0vQfrpM9h36Iho/ebtiaitq8Po64awwJGIiIioFdXU1GLq7Q8gIzNLFJ86aSz++/r/yZQVERERERERya1b1yi88PgcvPn+F+bY/sOp+OCLH/H8Y/dabR/ftTPiYzvjUOqpy7ZPwf5DR9E7vlur5NwcDhxJxd6D4jGM7l0i0SsuWqaMyNacycnH/pR0USwqIhhxUeEN7EH2TqVSwt/HA/4+Hlbrauv0F4oey6v/KX6svFD8WFqB2jq9eTu9wYBFm/Zj+nXxiO0Y1JrpExEREbV53/y8BO9++r0o5uXpgdULPodfe/tpUEJEREQkN/uofCJqQ/r37o7H59wmiu3YfQBf/bSk3u27do7A4L49RLG09Exs2rqzxXJsTgcOpyBpz35RrEvnCAzu31OehMjmGI1GrFy3yVzwC1yY3XLy+FF2U8BLLUutViPA3w+xXaIwecJojB4+xGqbnbv3Yu2ff8NkMsmQIREREVHbYzKZcO/j/4edu/eJ4n16dsMvX37A7/JERERERERt3ItP3o/YmE6i2Ovvfoa0jDNW2wqCgJtvGA03jYsovnDZapSXV7Rons2lorISC39fJYppXJxx8w1jODEjAbjQwW/1lmRRzEmtwqThffkcaaOcnVQI9vNC907BuK53FG4c0RNzpiTg2VtHoUtEoGhbg8GIpZsP4uDJbJmyJSIiImp7Nm1NwsPPvymKqVQqLP3+I3SN7tTAXkRERERUH3ZuJDsn9SS+bRa5vPn8I1ixbjNOZ+WYYy/8+yNMHjscocGBgFEn2n7a2EFITTuN4tJyc+yPNRvh4e6OQf16tVre1+p4Wjrm/7pUFHNxdsJtN05qYIBGwr+XQtn0fSUXQ0nYX5B4wa9Jf/VtWkIzvrT27j+M7Jyzotjg/r0R4Ne+/n8bDu5dO6l/MxsrGBwyqB+cnNRYu/FvUTz5wCHU6eowZeJY6RfT29hjtg8Sn2dSdpfzfVzydxW5SPyb2etLRMr7Id8XWp/dfubba94SyPlvJfnYUvaXr3hP2qOW9n5mMl2YlOT1/87DgqUrRetCgwOx8tev4erqYt7OZtj189QOtdnPzTb4b01ERERigtD83//s9XyGpLylHFjiY5YtbxlJfZ4YDfWGndVKfPP+6xgy+U7zxIQ1NbV44OnX8Nfv314YK1Rc+u3o6abBzOtHYP6SdeZYRWUVvv5xIR67/044OTlJy7MF1dXV4esfFqK8olIUn3n9SPh4ibu1maSMaQKAwlXCzjI+SZ08m7yroK+TdGiTQdr+TSc+N7J931EUFpeKYiMGdIOXh8Zq2/r2vyZSP4uk7C/lOa6UeD7JpJawr5S/t9RxSfGxVSolbhrZEyu2H8HhtEvXlJhMJqzYdhh1egP6x3a4GJR2bKOE/VUS/q0Fie+FUvdvKqlj0FKeK1I/P6Q8V6Tk7SQxb6NMr2vJn9cSvre01XPZkt7P6v8+2rjjSjgsIO39SOrzjNoIaa9LQcJ7uOSxPimvD0mfW1Kvk7vwuFNPpOOme5+CwSB+j/nif69g1ND+0j5nHI1cnx+Sz3tJyFvS91EZf5tL+t1Tz99L6m8harSWOO1sj/g3ILJv/NQgskFubq746r3XRLHyiko8/H//rrfrmMbFGbdPHWUV/2XxH9h74EiL5SnFqYxMfDX/N+gtftxNnzgK7Xy8ZMqKbE1ZWTn+2ibuQurr441hCf1lyojsRf8+PTF10lirQulDR49h6Yq1VieWiIiIiKj5/LL4D8z93yeimLubG1Yv/BZBgf4yZUVERERERES2ZlDfeDx67y2i2JbEZHz76+/1bt+newziu3YWxdIzs/D1jwuh18s04edV6PV6fPPTIpw6Le5I2b1LJ/TvGStTVmRr8otKsHPfMVEs2N8X/XtEyZQR2TqFQoGpw3qgb9cOVuvWJaZgx6F0GbIiIiIiahvyC4pw/W2PoLSsXBR//tF7cO9t02XKioiIiMi+sbiRyEaNHjYQd988VRRbvXEbFq1YX+/2XTqFY8roBFHMZDJh/oKl2LFrb0ul2SRHj53EJ1//hNo68SyYg/v3xOD+PeVJimzSzt37UFsrfp5MHj8KarWE2feozejZPQ43TZkEpVI8E1LqiTT8tnQldDpdA3sSERERUVNtT0rGfU/8SxRTKBRY+N089IjrIlNWREREREREZKve+tfj6BAaLIo998YHyDmbZ7WtIAi4+YaR8PJwE8VTT5zCl/MXoK5Org589avT6fD1jwuRcjxNFPd0d8UtU8ZYTdBIbdfabfthMF7qkKEQBEwe0Q8KqV3gyKEJgoCJCbFI6BFpte6v5BPYvPdkvZNnExEREVHTVVfXYOpdjyNTmyOK33j9GLz90hMyZUVERERk/3gmlMiGvf/6swjwayeKPf7iOygsKq53+7HD+mLMkD6imMlkwoKlK/H7yvWok7mQx2g0YuPfO/Dl/AXQWcye2qdHV9wydTwH8UgkO/ecaLln91h07BAmUzZkj2K7ROHm6ZOhUqlE8VOnM/HL4uWoqqqWKTMiIiIix3MqIxPT73wYdXXi354fvf0yJo65Tp6kiIiIiIiIyKa5u7niy3dfEcXKyivw6L/errcox9PdDY/eNR1uGhdRPPXEKXzy9U+oqKxq0Xwbq6qqGp9+8xOOHjspimtcnPHo7Jvg7ekuU2Zka0rLK3EmJ18UG9SrCwL9fGTKiOyJIAgY3S8GI/pEW63bfvAU1u9KZYEjERERUTMxGo2454lXsGvfYVG8f6/u+PGTtzg5CREREZEE/CZFZMN8vD3xydvijheF54vx9Cv/aXCfKWMScN3AeKv4X9sS8fYHnyM9M6vZ82yMc/kFeO/Tb7F8zZ8wXjbrJADEx0bjrpk38McdWamuqREtx3S2nnWS6GqiOnXE7TOnwkntJIqf0ebg3U++wve/LMaOpGTkFxRycI+IiIiokWpra5F7Ng+HU45j87ZELF6+BpNuvg9F58WT8Tx2/114dM6dMmVJRERERERE9mDciMG486bJotiK9X9j6cr19W4fHNAeD985Dc5OalE8PTML/37vUxxOOd5iuTbGkdQTePO9T3Eq44wo7uykxiN33YjQQD+ZMiNbVFYhnohTpVRieL84mbIheyQIAob16oxxA7tarduTkokFf+7F4VM5qKqxre62RERERPbm1f9+gkUrxL9Tw0OD8MdPH8PVVSNTVkRERESOQXX1TYhITjdePwbTJo7C8rV/mWO/LFmFW2+cjPGjhlptLwgCZkwYBo2zM9Zt3SNal19QhA8++w4jhw3C5PGj4KRWW+3f3IxGI/7amohVGzZDb9GtEQAG9O6O22+cBKWShY1kzbK4UWMxCy9RY0WEh+HOm6fjl8XLUVNba46bTCZkZecgKzsHm7bugJenB6I7RSKqUwQ6dgiDuhXeJ4mIiIjsyY+//Y5X//MRsrJzr7rtpLEj8MG/X2yFrIiIiIiIiMjevf/Gc1i3eQcKii5NmvPYC3Mxatgg+Pp4W20fERqIx+6+EZ//vBxV1ZfO+5eVV+DL+QvQr3cPzJw6EW6urq2RPoAL3RqXrFiH3fsOWq3TuDjjkbtuRGR4cKvlQ/ahuqZWtOyqcYJazUt56NoN7NYRTmoVVu84KprQ9ZQ2H6e0+RAEASF+3ogO90d0uD/8fTwgCIKMGRMRERHZnpLSMixZuQEn08+g6HwxCs8Xo7CoBIXni3HqtLixiIe7G1b/8hkC/dvLlC0RERGR4+AZUSIbJwgCPnn7X9i8Yw9Ky8rN8QeffQ1Htq+Ch7tbvftcP2ogPNw1WLZhB/R6g3mdyWTCX1sTcejoMYwYOggD+/RskYKxOp0O+w8exebtScjOPVdPjsCY4YNww9jroFDwhDlZMxqNqLEYzNO4OMuUDTmC0JAg3H3rDPy8aDkqq6rq3aa0rBzJBw4h+cAhqFQqdAwPQ3TnjojqFAFvL69WzpiIiIjItpxIy8B9T7xY78Q1luK7dcWCrz+EUqlshcyIiIiIiIjI3rXz9cbHb/0Ltzz4vDmWX1CEZ199B99/8k69+3QMC8JT987Epz8uQ2l5pWhd8v7DOJGWgZlTJ6Fn965QKFpuolWj0YjDKcexaPka0XjuRZ7urnh09k3s2Ej1qq4Vd9NzceZ4KDVd75gwOKmUWL71EIxGk2idyWRCdn4xsvOLsXnvCXi6uSAqzB9RYf7oGNwOTiyqJSIiojautrYOo6bfgwNHjl11W6VSicXfvo9uXaNaITMioqsQhAu3to5/AyK7xjNTRHYgONAf7772NO5/5g1zLCv7LF566yN8/J+XGtxv+IB4RHcMxc/LN+FMTp5oXWFRMZb8sRYr1mxE/z7xGJbQH6HBgZJzLSg8j+1JyUhK3o/Kqup6t/Fv74vbZ0xC54gwyccjx1VbWyeaURIANC7s3EjSBAb44747b8bGv7fjZPrpK16Yr9frkZZxGmkZpwEA/u3bI6pTBKI7dURYaHCLXghBREREZIu++2VxowobgwL8sWrB1/DwcG+FrIiIiIiIiMhRzJwyDguWrcGqP7eaYz/8tgy33DgZY64bXO8+wQHt8fyDt2LBik1IOXlatK6svALf/rwI7dv5YOigfhjUrzfc3Zqvk2NlVRV2JR/AtqRkFBSer3eb2KgI3DZtHHy8PJrtuORYqmvExY0aFyeZMiFH0a1TMNT/FDjW6gwNbldWWYN9x7Ow73gWVEolOgT5IvqfYkcfz9brektERERkK5at2diowkYA+Pjtf2HciPp/pxIRERHRtWNxI5GduPfW6fht2Vr8vTPZHPvsu19x87SJSOjfq8H9gvzb4Zn7bsJfifuxZvMu6A1G0fo6nQ47du3Fjl17EREWgk6RHRAeGozw0GD4tfO9YvGOyWTC+eJSZOXkIis7F6cztUjLyLQqSLtIEIARg/vjhrHD4eSkvsa/ALU11TU1VrGW6DJKbY+PtxdmTrseOp0Op89ocfLUhQLG+mZTvlx+YSHyCwuxc/deuDg7o3NkBGJjotA1pjMEzvhCREREDk6v1+PnxX9YxQVBgK+PN9q380F7Xx9EdeqIl595GKEhQa2fJBEREREREdk1QRDw2TsvYWvSPpSVV5jjDzz9Cg5vWwV3d7d69/P2dMdDt0/B7oOpWLp2K6prakXrC4uKsXz1n1i1fjP69uyGwQP6IDwsBGrVtV8uodfrcSY7F4m792HvgSPQNTAJkIuzE2ZMHIFBfbpxDIGuyLJzo8aZxY0kXUyHADx203AcO5OPk1n5yDxbBJ2+4UJHvcGA9OwCpGcXYF1SCvy83dE5zB/RHUMRFuALpZKTvhIREZHj++G3Pxq13avPPoSH7p7VsskQERERtTEsbiSyE4Ig4Kv3XkOPETei5p8BOZPJhDlPvYL9m5fB+QqDHEqlAmOH9kX3mI74dcXfOK3NrXe7TG0OMrU55mUXF2eEBQfBy9MDarUKSqUSBqMRep0e5ZWV0GafRWVVVaPyZ7dGulbV1eLiRpVKBVUTBpmJGqJWqxHdORLRnSNhMpmQX1CItPRMnEw/DW1OboOF2gBQU1uLo8dO4OixE4iMCMfUiWPh6clZl4mIiMhxrf9rG87lFYhiezYtR+/4OCiVSnPsSt+hiIiIiIiIiK4mNDgQ/33lKTz0/JvmWGZWNl75zzx8+NaLDe4nCAIG9opDl04d6u3iCFwoTNy19yB27T0IpVKJ4EB/86Sv4aHB8PBwh1qlgkqphN5ggE6vR0VFJbKyc823nLN5MBgaLhACLnRrvHXqWPh6ezb9D0FtBjs3Uktx0zijb9cO6Nu1A3R6AzLPFiFNW4C0rHyUVFz5Oo+CkgoUlFQgKSULGmc1hveJQf+4jizWJiIiIoeVlZ2LTduSRLGZU8aje2w02vv6oL2vN9r5eiM6MhzBgf4yZUlERETkuFglQmRHOncMx9znH8Hzcz8wx46dTMdbH36JuS88ftX9g/zb4dkHb8fJjCxs27Ufh46lwWi8QvFOTS3SMjIl5dwlqiOGDeyD7l2ioFTyRDc1nmXnRo2LCwdLqMUIgoAAfz8E+PthyKB+qK6uwamMTKRlZOJURiaqqqsb3DcjMwtffP8Lrh83CnFdo1sxayIiIqLW8/2vS0TLA/r0RL/ePWTKhoiIiIiIiBzZnNtvxG/L1mLbrn3m2Mdf/4ibp0/CgD7xV9z3YhfHfUfTsWHbLuSeK6h3O4PBAG3OWWhzzmLn7n31bnOtggPaY8ywAegf34VjWtRoNezcSK1ArVIiKswfUWH+MA2KRWFJBU5m5SMtuwDac8UwmowN7ltdq8P6xKM4mZWHqcN7wcPNpRUzJyIiImodPy5aIZrE1cvTA/M/fgsajcV3nyt8byIiIiKipmNxI5GdefL+27FoxQbsO5Rijr0z7xvcdMOFWWKuRhAExHTqgJhOHVBSWo4dyYewM/kQSssrmi1HjYsLBvXtgaEDeiPAr91la9jBgxrPqrhR4yxTJtQWaTQu6B7XBd3jusBoNCIn9xxOpp9GWvppnMu3vhCiuqYGS1aswcn0DEwcMwLOzny+EhERkeMoKCzCqvWbRbHZt90oUzZERERERETk6BQKBb5+/zXEj5yB2n8Kv0wmE+Y8+RL2/rUMTk5XLv4SBAH9esaib3xXZGTlYNuuA9h/9DgMhua/CFWpVKBXXAyGDeiFThGhF4oaTVfu7Eh0uWrL4kZ2bqQWJggC/Hw84OfjgcHxnVBTq0N6TiFOZuUjPbsAlTW19e6XkV2AL5b+jeuHxiM2MriVsyYiIiJqOUajET8uXCGKzZo6wbqwkYjIRgnChVtbx78BkX1jcSP9Q+K7uaTdpews9VPI/j7FVGo1vv3wDfQbezP0ej0AQK/X474nXkLi2l+hVCqvuL/JqDf/v5eHBpNGDsT44f1wPD0LmdqzyMrNQ1ZOHsoqKhudk6vGBeEhgQgPDUKH0CDERXeCk5P64hEvO7jU4kYJ+0s6toxFmYJC2v4KCW/zUv5mUmcoEgRU19SKvmlqNJpW+uYp5d9bxvcUya8vaohCoUBYaDDCQoMxavhglJWV42T6aWxL3IOy8nLRtoeOHsMZbQ6mTRqHDuGhMmV8BVKfJ1Jeg5KfoxKOLfWlabcvLxnfk3imgBqDzxOyCzJ+/kjSvJ/Zvy5Zaf79CQAuLs64efrkph+j4YO3wH2STZLzM4C/nYiIiEhWApr/x4KE7zeynu+TQFLeUg9up39vOzwPE90pAq899yhe/PcH5tjRYyfxzrwv8eqzj1x1/4tjopFhgYgMm4DpE4Yhad9R7DmYinMF5yXnF+Dni/4945DQtwe8PNzFK6WM70kdG5RExmM7ezR9X5W0CSeFmpKm71xXfvVtGnCxK0y1ziQa09W4eQIqzdXvwFB39W0actk1A00i5aki5f1MIfW9UELhseLK12Fc+bgSX1smKecor35sF40ScZ1DEdc5FCaTCbkFpTiZlYc0bT7OFpaJtq2uqcOSjcmIjw7DhIQ4OJuvC6kvbyn/1lKvYZLyN5Pwby2VScJzxSTxEkAp7wtyfseR8lyR8tpUXuG53xgKuQrZJV7Lo5DwmSv1byaJlO/wEt8T5Hp92OsYFdkVqd3qTZKe4xKObbQ+7vakvcg4oxXF7rllWv2vQ0nXVHKMqlXZ7XiknNd7NfN5AVnPcRARkb1hcSORHYqP64LnHpmN/8z7xhxLPngU8775BU8/eNc1359SqURcdEfERXc0x0rKKqDNzcPZvELU1Omg0+mhN+ihUjlBrVbDSa1CgH87hIcEoZ2Pl+Qfq0SWLDs3urATHtkIT08P9O3VA3FdorF6w19IOX5StL6ktAw//LYUQwb2w3VDBl616JyIiIjIlplMJsxfsFQUm379OHh5SrjokIiIiIiIiKgRnnn4bixZsQ4Hjhwzx9764CvceP1YxHWJuqb78nR3w7jhAzBu+ABU19RCm5uPrNw8aHPzkJWbh/zC4gb39W/ng/CQQISFBKBDSBDCggOgceG4FTWPmhqLzo3O7NxI8hEEASH+3gjx98aIvjHIyC3Cii0HUVYpHrs/dFKLM2cLMW1Eb4QH+sqULREREVHzmP/bctFybEwn9OvVTaZsiIiIiNomFjcS2alXnn4Qy9ZswolTpy/F3vkEU8ePRGREmOT79/Z0h7enO7p36SSKCxJnviRqrOrqWtGyRuMiUyZE9dNoXDBjykTEdI7Emj83o7bu0uCzyWTC9qQ9OJWRiRtvmID27TioR0RERPbpwOEUHE45Lordc9sMmbIhIiIiIiKitkStVuPbj/6N/mNnwmC40HFNp9NhzlOvYPvqX5s8uaDGxRnRkWGIjrw0pmowGFCn00On10OvN0CtdoZarYKTWsVJDKlFVVsWN7JwlmxIZIgfHrxxOFbvOILUjFzRupLyavywaicGx3fGdX1ioFSyKwsRERHZn/KKSixdtVEUu/vmqWz2QURERNTKWNxIZKdcXJzxzQdvYNgNd5pj1dU1ePC5N7Bh8Tf8cUV2z7JzIwfyyBYJgoAe3boiPCwYy1dvwBltjmj92bx8fDV/AYYl9Ie3lyeMRiOMJiOMRtOF/zffLiwbjAardUqlEh07hKFTxw58byciIqJW9/2v4q6NHcJCMGLoIJmyISIiIiIioramV49YPPPwbPzvk2/NsV17D+Gz7xbg8fvvaLbjKJVKaJRKaHBhPEpQ8FIKankmkwnVtRYTvrqwcyPZFo2LE2aM6o0jHQKwducR1NbpzetMJmDHwVNIzynA9BG90d7bvcH7MZlMMJlMF8ZCTaZLY6QmEwDATePMsVAiIiJqdYtXrEdVdbV5WalU4vYZk2XMiIiIiKht4hl5Ijs2ZEBvPDT7Znwxf6E5tmnbLvyw8A/MvmWajJkRSVddbVncyM6NZLu8vbxw1y0zkLRnPzZvTzTPIA0AOr0Of23bKen+d+7ei5CgQIwYOohFjkRERNRqampqsWDpSlHsrpunQ6HgLOxERERERETUel577hEsX7MRaRlnzLGX3voIUyaMRIewEBkzI5Kmtk4H0z+FXRdxwleyRYIgoEdUKMIDfbF8ywFknT0vWn+2oBRf/r4Vbi5O/xQummBUKC8rZLxQzHglnu4aJPSMRp+uEVAp1S35cIiIiIjMflj4h2h50uhhCPRvL08yRERERG0Yr0YjsnP/eelJhAYHiGLPvPYu8guKZMqIqHlYdW7UsLiRbJtCocDggX1x3x03w69du2a//5yz5/DL4uX4/pfFyMjMshrsJiIiImpuK9dtQnFJqSh29603ypQNERERERERtVUajQu+/mCuKFZZVYUHn3md58rJrlXX1FrFNM7s3Ei2y9vDFXdNGoRR/btCqRRfcmYwGFFWWYOKqlpU1dShplaHOp0eer3hqoWNAFBWUY31Ow7hk9/+RPLRdBgMV9+HiIiISIoTp05j554DotjdN0+VJxkiIikEAQJvAJuGENk1FjcS2TlPD3d88b9XRbGS0jIsXb1RpoyImgc7N5K9Cgr0x/1334IBfXq2yP1rc3Lx08Lf8eNvS3EmK7tFjkFEREQEAPMXLBUtXzdkADp2CJMpGyIiIiIiImrLhg/uj/vvmiWKbfh7BzZv3yVTRkTSVdfUiZaVCgXUapVM2RA1jkKhwJCenXHvlCFo7+3e7PdfVlGNtdsO4NMF67E/9TSLHImIiKjFWHZt9Gvni0ljhsmTDBEREVEbx7OiRA5ArzdYxbw8mv8kMlFrqqkVz1TKzo1kT9RqNSaMGYGYqE7Ys+8gis6XQKlUQKG4/CZc+K9gsXzZekEQcEabg/PFJVbHyMzKxvwFS9ApogNGDB2E0JCg1n+gRERE5LCyc85iw+btotjsW2fIlA0RERERERER8MT9d+DrHxeJYkdST2LUsEEyZUQkjWXnRo2L84VOA0R2IKi9F+6fPgwbd6dib2ommruRbkl5FVZt2YedB05gWN+u6B4VBoWCc/gTERFR8zAYDPh5ySpR7LYZ10OtVsuUEREREVHbxuJGIjtXW1uHZ19/VxQLCwnEtImjZMqIqHlU14g7N7o4O8uUCVHTRUaEIzIiXNJ9GI1GHDqaiq07d6OktMxqfXrmGaRnnkFUZEeMGDoIwUEBko5HREREBAA/LVoO02VXJHm4u+PGyeNlzIiIiIiIiIjauv/O+0a0rFAoMG7kEJmyIZKuulbcuVHj4iRTJkRNo1YpMXFwdwzvHY3cghIoFAoIAqAQBCicNf9M9CpcNunrP/8vCJcmfxUEFBSXY8veY8jMKbA6xvnSCvzxVzJ27DuO4f1iEdc5lEXAREREJNmfWxKRey5fFLvnlmkyZUNERERELG4ksnOffr8A6ZlaUeydl5+Gq6tGpoyIpDOZTDAYjKKYWs2PLGqbFAoFevXohh5xXXHwSCq2Je5GaVm51XZpGaeRlnEaXaI64bohAxEY4C9DtkREROQITCYT5i9YKorNmjYRbm6uMmVEREREREREbV3ygSP4afEKUeze22ega3QnmTIikk6n04uW2SWG7JWbxhlR4RYTsDo1/pqVDhpn3HXDUJzOKcDfyanQ5pVYbVNYUo7fN+7G9n3HMLxfLLpGhrDIkYiIiJps/m/LRct9e8ahW9combIhIiIiIlaKENmxgsLzePP9L0WxgX3icfO0CTJlRNQ8jEajVUypVMqQCZHtUCqV6NOzO+K7dcX+Q0exPSkZ5RUVVtsdT0vH8bR0xMZEYcTQQfBr306GbImIiMie7dy9D6cyzohis2+dIVM2RERERERE1NaZTCY8/fI7opiHuxvefOFxmTIiah4GizFRFcdDqY3rGOKHiOBhyDhbjM27jyI3v9hqm/zzZViyYRcC2nthRL84REcEsciRiIiIrknR+RKs3PC3KHb3zezaSET2SxAu3No6/g2I7BuLG4ns2Kv/+xRl5eLClg/f/D+euCW7ZzAYrGIsbiS6QKVSoX+fnujVIw57DxzGjl17UVlVZbVd6ok0HE9Lx01TJqFrTGcZMiUiIiJ79f0vS0TLMVGRGNS/t0zZEBERERERUVu3ZMV67NyzXxR7+ZmH4O/Hyf3IvhkM4uJGpVIhUyZEtkMQBHQKC0BkqD/SzpzD33tScK6wxGq7vMJSLFyXiGB/H9w4ZgB8vdxbP1kiIiKySwuWrUFdnc687OzshFvYUISIiIhIVrZb3ChncZZcxzaZ5Dmu7KT8vaX+W0nZX+q/l4RjCwocST2Jb35eKgrfPmMyBvTteeV9TdZFY9fEKGF/QcbBGJN1J8Br2FnisSXsL/lPJuV5Ju05KoXBqIdl7kqlGhAaUeAo6d8akPbvLWVfFiXTtVGr1RjUvw/69OyOPfsOYefuvaiuqRFtYzQasT1pj20UN0r9fiXpe5LEY/Pl2QRy/dFk/MyWFT9/qBVIeh+X8Xkm+XVtr+8LTafT6bBk5TpR7O5bbuQkOkRm/NwlIiIiImpN1dU1+L+574likRFheHzOHTJlRNR8LDs3KhT83Uh0kSAIiI4IQlSHQBw/nYstyanILyq12i43vxiLNyThwZljZMiSiIiI7NEPC/8QLU8dPxI+3l7yJENEREREAGy5uJGIrujrnxfDeNlgh0bjgrdffkrGjIiaDzs3EjWek5MThgzqh369e2D33oNI3LMPNbW15vXFJdaDfEREREQNOZdXgIqKSlHslhsny5QNERERERHZLFknM5NAct7Nk8Y1a7P1TgK+/WUpzmhzRdF3X38ezs7OV9nXPic8EiROIGqSdQK3ph9b0uNWuTR9XwAmN/+m76x2a/Kugq4SRoULoLz0XFY5u0Fw9mzcHdRVXn2bBpj01U3eFwBgkDK5r4TLlKS+hxv1Td9XyhzSUieBlnMSaSlj9M00WZogCOgaGYIuHYORmp6NLXtSUVhSLtomr7AUNbV1cHF2aoYDyviYTRKOLXWic7lILuqW8PpQSNlX3fR9AWmvawn7Sp7EUNJ7eBu95kfSv7W9/giQr2GElO/Ckl8fUr6Hy/pPbacTvUr4e5eUluHAkWOi2Oxbp0nNyA7I2QimDZLyniLn73opjUXk/N1CREQOgZ8kRHZKYXGSzcXZCZ4e7jJlQ9S86i9u5EcW0ZU4Oztj2OABuOuWGaJ4dU2NqBieiIiI6Eqc67kAyFUj7SJBIiIiIiIioqbad/CoaDk0OBBTJ46WKRui5mXZuVGpaKOFH0SNIAgC4jqH4aGbx2DKyL5W66tr6mTIioiIiOxNXZ3OKhYfGyNDJkRERER0OVaKENmp2TeLZ4spLinDJ9/+IlM2RM3LciAPYOdGosZyd3O1ilVX18iQCREREdkjjYt1IWMVv0sQERERERGRTIYO6idazs49h7937JYpG6LmZTBYFDdysleiq1IoFIiP6QClxYTgVSxuJCIiokZwddVYxapramXIhIiIiIguxzOjRHaqZ/eumGYxK+kHX/yAsvIKmTIiaj71dW607FZKRPWr7yRcVXW1DJkQERGRPdLU06WxqorfJYiIiIiIiEged8y8AeGhwaLYG//7FCaTSaaMiJqPwSgeE2VxI1HjCIIAV42TKFbFogQiIiJqBI2Ls1WME70Skd0TBN4u3ojIbvHMKJEde/XZh0XLxSVl+Pgbdm8k+2dZ3KhQKFjcSNRISqUSzk7iwTx2biQiIqLGUqlUcLL8LlHD7xJEREREREQkDycnJ7z41AOi2PZde7F5+y6ZMiJqPladGxVKmTIhsj+WhQnV7NxIREREjaBUKuHszLFQIiIiIlvDShEiOxbfrQumTxojin345Q8oLSuXKSOi5mE1kKfkQB7RtbDsuMTOjURERHQtLC8M4mylREREREREJKe7b57G7o3kkKzHRHkJD1FjubpYdm5kcSMRERE1jsbF8roqjoUSERERyY1nRons3CvPPCRaLi4pwyff/ipTNkTNw7JzIwfyiK6Nq0YjWuZJOCIiIroWrq4W3yWqOFECERERERERyae+7o07du/DX9uSZMqIqHkYjBwTJWoqjbNlcWOtTJkQERGRvXG1mjSe11URERERyY1nRonsXH3dGz/4gt0byb5ZFzeycyPRtbAqbmRBAhEREV0DywG96hoO6BEREREREZG87r55GjqEsXsjORarzo0KjokSNZarxlm0zM6NRERE1FhWY6EsbiQiIiKSHYsbiRzAq88+LFouKS3Dx9/8IlM2RNIZjBzII5LCqttSNYsbiYiIqPE0LpazlfK7BBEREREREcnrQvfGB0WxnXv2s3sj2TWr4kZ2biRqNFcXi86N1ezcSERERI2jcbGYJIHFjURERESy45lRIgfQIy4GN14/VhT78MsfUVJaJlNGRNIYLYsbOZBHdE3YbYmIiIiksO4Cze8SREREREREJL+7Zk216t74+n8/YfdGslsGo0G0zDFRosZztShKqK5l50YiIiJqHKuxUE70SkR2ThAE3v65EbVlf/75J7777jtUVFTInUqT8MwokYN45ZmHRMvs3kj2zGAQD+QpFPy4IroWGovixqoqnoQjIiKixnN1ZedGIiIiIiIisj1OTk546SnxmGhi8gFs2pooU0ZE0lh1blQoZcqEyP5oLDs31rC4kYiIiBrHetJ4doAmIiIi+/fll1/ivffeQ0lJidypNAmrRYgcRI+4GMyYLO7e+NFXP7F7I9klq4E8JQfyiK6F9Qxj7LZEREREjadxYRdoIiIiIiIisk133TwVEeEhotgb//uU3RvJLlmPifISHqLGcrUobmRRAhERETWWRiPuAM3rqoiIiMgRlJeXAwC8vb3lTaSJVHIncIEAsA2s9L+BrAM2EnKX9LClPm8k7C/57y1l//r3feWZh7B01Z/m5ZLSMsz7+me89tzDlzYy6iUcFzAJTS8yE5ROV9/oyvcgYVcpzxWp/9bGq2/S4K6Gq29zJVIetyBh8EzC8wQADEaTKHelUiUtn2thkvg3b/qBJe7Pz1G6xLq4sa13W2qrF7XwfcG+tNXnKZGtk+u9VN73BKvvElUc0CMiIiIickwyjp1IGb+Qc0xUrjFtyQ+5+cclG0/Kv7X1mI1apcBLTz2AOU+9ao4lJh/Axr+3Y+yIwZc2lDA8B0DauJRCymUY0p5jgiDf68MkYVxTSnGq1JelIOEOTE5uUg4Mg6AGLhtDVzq7A07ujdtfyjiy1PFQKfsbJOwrcQwaSrWEfeUaQwbQFicCbsR7gquzk2i7qupamIzGC69pub4rSH2OQsrrWuKhpeQu6boUqW/iUvKWsK+ddtqV8lkNAILK5eobNbgzC/jJtkmdLEXKd0q7ve7Xjq9x4HVV18perz+y3+coERFRU5SWlsqdgiQ2UtxIRM2he2w0brphHJas3GCOffTVT3ji/tvh7eUpY2ZE18ZgMajFWUqJro2rq/gkXFlZBSorq+Dm5ipTRkRE0hiNJuQXnkdW7jloc/NQWlaBOp0eer0BJpigVqngpFbBw90NYcEBCA8JRKCfLxQKfocgagpXV/EFChzQIyIiIiIiIlty58wb8NaHXyEzK8cce+PdzzHmugRpFxUTtTKDkWOiRE3lqhFPrm0wGFFbp4OLs9RJt4mI5GMymVBSVolzhcWo1RlQp9fDYDBAqVRCrVLC2UmNwPa+8PFy5/deIglcNZZjoZzolYiIiOxbamoqysrK7Pp3AosbiRzMK888JCpuLC0rx0df/YzXn39ExqyIro11caN9znpHJJd2Pt6iZZ1ehzV/bsbMadfLkxAR0TUyGk04fioTqWmnkZVzoaCxtk53TffhpFYjNMgf4SEB6NI5AnHRkbw4iKiRLGcrra7hgB4RERERERHZDrVabdW9MWnvQWzckiju3khk4wwGcYtRjokSNZ6bxgWCIIg6bW3dewzjBsfLmBUR0bUpKa/EmZx8ZOUWIOtsAbRnC1BeefUJJ91dXRAW7I/wID+Eh/gjIiQA3p6N7P5MRNBYFDdW19TKlAkRUfMQBOlN0R0B/wZk67Kzs6HValFeXt6sXRazsrKwePFi87K7u33+NmBxI5GD6dY1yqp747yvf8aTD9zB7o1kN3R6vWiZA3lE18bT0wOxMVFIPZFmjqWeSEPKsZOI6xotY2ZERFdWUVWNpL2HsX3PIRSeL5F0X3U6HTKycpCRlYMtSfvh4+WBIf17YnDfHvD0cGuehIkclMbFYrbSKhY3EhERERERkW25c+YNePvDr3E6K9sce/1/n7F7I9kVo9GiuFHBydmIGstJrUJEiB9OZ+ebY7sPn0Jsp1CEBQfImBkR0ZUZjEYcOZGJbclHceJ0ztV3qEdFVQ2OncrCsVNZ5lhURAiG9e+O+C4doVTxOwXRlViPhV69qJiIiIioKf7880989dVXSE1NbdHjXD75k71icSORA3rlmYewdNWf5jcpdm8ke1JeXoHE3XtFMWcnJ5myIbJfE8eMQGZWNqqqL52AW/PnZkSEh8LNzVXGzAi4cMFCXmExtLnnUFpWAZ3eAJ1ODxNMcFKpoFar4OHuhrDgAAT6tWO3OXJ4mdlnsW3XAew9fAx6veHqOzRBcWk5Vm3cjrWbd6JXXDSGDeyNzhGhLXIsInvn6moxoFfNAT0iIiIiIiKyLWq1Gi89/QDue/IVc2zXvkP48++dGDdyiIyZETXO+eJSFJeWiWIKFjcSXZMJQ3ri66V/mccVTCYTVmzeiwdmjYNazUvibEWdzoCKmjro9Ebo9BeKutUqBdQqBTxcnaFWcbJrahtKyiuRuP8YduxLRWl5ZbPff1pmDtIyc+Dl4YqEPt0wuE8cfLw8mv04RI7A1bJzYzUneiUiIqLmVVFRgbvuustc1NjSxYeCINh9gSPP5BA5oIvdGxevWG+OffTVT3ji/tvh4+0lY2ZEV2YwGLDkj9WoqBSfxIuJ6iRTRkT2y93dDRPHjMDSlWvNsarqaqz5czNumjqJM1e3stKyChxPPwNt7jlk5eRBm5uH2jpdo/ZVq1UIDfJHeHAgwkMCENOpA3y92Y2ZHENlVTWWrP4Lew42bmYiL08PhIcGIyjAD87OTlCpLvyk1ev1qKvT4Vx+IbKyc1FcUtrgfRgMRuw9fBx7Dx9Hz7ho3HzDGHZyJLLgqtGIlqtrOKBHREREREREtueOmybj7Q+/RsYZrTn2+rufYeyIweApcLJldXV1WLRiPeosxgmCAvxkyojIPvn5euK6frHYlHTEHCsqKcfmPUcxbnBP+RJrw2rr9MguLEdWfjm0+WXIyi9DXnElGrq8UiEICPR1Q5i/J8L/uYX4ecCJBY/kQKqqa7F8YyJ2HTpp1bW5IRoXF3h4uEGtUkGpVMJgMECn16O8ovKqRVil5VVYt2UPNmxLRr8eMZg+bijc3TRX3IeorbEqbqyplSkTIiIickTl5eUYPXo0ysrKYDKZIAiCQxQftjQWNxI5qFeeeQhLVm4wvwmWlVfgo69+xhv/96jMmRE1bOPf25CVnSOKdY6MQI9uXWXKiMi+xXWNRsrxkzh28pQ5lnoiDSnHTqJbbIyMmbUNJpMJJzOysG3XARw6lgajsWk/THQ6PU5n5eJ0Vi4AQBCAuOhIDBvYC7FRHTmTM9mtw8dOYcEfG1B2hZlJQ4MDEd+tK8JDgxEeGgwvz8bNLlpeUYms7FxkZeficMpxnNHm1LvdwZSTSDutxazJo9GnRxcWfhP9Q+Ni0bmxisWNREREREREZHsudm+894mXzbHd+w5jw987MH7UdfIlRnQFJpMJq9ZvRl5BkSjeIzYaPpzYkOiaDYqPwrGMXOTkXXpN7T6Uhq6RoQgPai9jZm2HTm/AgVN52HE0B6fPljRYyFgfo8mE3KIK5BZVYPexC2OhCkFA5xAfDOkeivhIfyiVHAsl+3XkRCZ+W70VpRVVDW7j4uKMDqHBCAsNRofQEISHBqN9O596xy1NJhOKzhebx0GzsnNxRptb7ySVRqMJuw8eR2raGdw8eSR6xnJieaKLrMZCq6tlyoSIiIgc0csvv4zS0lLzd3qTyYSwsDCEhYXBw8MDXl5e8PSUfh6wrKwMAJCdnY3ExETJ9yc3FjcSOai4Lp0xc8p4LPpjnTk27+uf8eQDd7B7I9mkIynHsCt5vyjm7eWF6ZMnsnCHqIkEQcCksSNxRpsjOhG3duPfiAgPhbs7O5W1hKrqGuw+kIJtuw8gr+B8s9+/yQQcPZGBoycy0M7HC0MH9ERCn+5wd3Nt9mMRtYTKqmosWbO5wW6NKqUSveLjMCyhPyI7hDWp4NDD3Q1xXaIQ1yUKE0YPxxltDrYl7sHeA0eg0+ut8vl+0SrsP3qCXRyJ/uHqKp7BlwN6REREREREZKtun3E93vrgK1H3xjfe/RzjRg7nRFZkk5L3H8aRYydEMf/2vrh+zHCZMiKybwqFAlNG9MHXS/+CXm8AcOGiwZWbk/HAzDFQq3lpXEspLKvGjqPZSErNRWWN7uo7NJLRZMLJ7PM4mX0enm7OGBwXgoS4UPh4uFx9ZyIbUVVdiyUbErHnyKkGt+nYIQzDEvqjd49YqNXqRt2vIAho384X7dv5ond8NwCATq/HwSOp2Ja4B+mns6z2Ka+sxjcL16BP92jMnDicXRyJALi6WhY3cqJXIiIiah5arRYbNmyAIAjw8PDA/fffj3HjxiEsLKxFj7t+/Xo8+eSTAICKigq4u7u36PFaAs/gEDmwV555EItXrBd1b5x0y0NYOv8jBAf6y5wd0SV5+QVYuXajKKZSqTBr+mSrC6uJ6Nq4u7th4pgRWLpyrTlWVV2NpOT9GDNiqIyZOR6j0YQtSfuwauN21NY1bgDP28sTgQF+cHZyMg+u6nR66HQ6nMsvxPnikivuX1Rcij/Wb8WaTTsx7rqBGDd8AJRKpdSHQtRiMrPP4qtflqO0nm6NGhcXjBkxBIP794aHR/P+uO4QFoI7Zk3D9MnjkLTnAP78ezsqKsUzpF7s4jjn1imIjgxv1uMT2RtXjXhAr74Zf4mIiIiIiIhsgVqtxsvPPIB7Hhd3b/z8+wV45N7bZMyMyJo2JxfrN28TxZydnTBryng4OTWuqIGIrPn5euK6frHYlHTEHCsqKcf2/ccxckA3GTNzTLlFFViReAqpZwqvqUsjAAgCABMavV9ZZS3W7cnAhuTT6BHphymDo+HnzQlfybalpmvxy8pt9XZrVKvV6N+7B4Yl9EdYSFCzHE+tUqFfrx7o16sHsnPPYXtSMvbsO4TaujrRdvuOnMTJDC1unzoa3WI6NsuxieyV9VhorUyZEBE1D0EQOMkXwL8B2YQNGzYAAGJjY/HDDz/Aw8OjVY47fvz4VjlOS2JxI5EDi43pjFlTx2Ph8kvdG3ftO4S+o2/Cku8+xOABvWXMjuiCmpoaLFq2Ejq9uBBo0thRCAoMkCkrIscS1zUaqSfSkHoizRzT5pyVMSPHk19UjJ+XrkP6mewGt3FSq9EluhPCQ4IQHhaC8NBgeF6lgKuishJZ2WeRlZ2LrOxcnEjLqLfARKfXY/WmHTiUmoY7bpyA0CBOYkC250RGFr78eVm9xb9xXaJw201T4O3l2aI5uLm6YvR1gzGgTzwWLluNA0fE3SMrq6rx6Q9LcN8tU9Cja+cWzYXIlmlcLGYrrWJxIxEREREREdmu22dMxlsffIX0zEvdGx974U2knDiFj/79Lzg5OcmYHdEFFRWVWPzHWhiNRlF82oRRaOfrLU9SRA5kUHwUjmXkIievyBxLScticWMzMhiN2Lj/DNbtyYDB2HB5okIAgnxdEdbeHeF+bgj3c0eAtwZOKgWUSgVMJhOMRhNq9UacPV8FbWElsgoqoC2owLmSapjquWujyYSD6flIOVOIKQlRGBYfDgUvXCYbtPtwGn5ZuRXGep7IcV2icOuMG+Dj7dVixw8NDsQtN07GxDHXYcHSlTiSKu4WXV5ZjS8XrMatN4xEQp+4FsuDyNZZjYWycyMRERE1k8TERAiCgB9//LHVuyeGhYUhO7vha5htHYsbiRzcmy88jnV/7UBpWbk5di6/ECOnz8a8t17EA3fN5EwFJBuTyYQ/Vm+w6kzWp2cP9IrnIANRcxEEATGdI0XFjTpd4zoL0pVd7Na44s9t0On09W4T4N8ewxL6Y2CfntBYzP52Ne5uboiN6YzYmAtFVrW1ddh78Ai27tyN7NxzVttrc/Pw389/woQRCRg3fCC7OJLNOHoiA18v+AN6vUEU17i4YMaUCRjYt2erfif18HDHnLtuxr6DR7Fo+WpRF0e93oCvf12O2TMno0+PLq2WE5EtseyeXlVdLVMmRERERERERFenUqnw2nOP4M5HXhDFv5z/G46knMCS7+chMMBPpuyIAIPBgCUr1qG8olIUHzqwN7pEsXMSUXNQKBQY0T8Wv6zabo5V1dRdYQ+6FrlFFfh5Uwq0BeUNbhMT4oUhcYGIC/eGk6rhMUpBEKBUCnBVKtApyBOdgi5NfFmrM+BI5nlsTzmH9HPWx9LpjVi67QQOnMrD7aO7wc+ndTpgEDXGluQULFmfaBXXuLhgxg3jMbBfr1YbD/Xy9MCDs2/Fnv2HseSPtaJxHpPJhF9X/IWqmlqMHszGCNQ2WXZu5FgoERERNRetVov77ruv1QsbL1dSUiLr8ZuKxY1EDq5Tx3BsXfEjpt31OE5nXarE1un0ePj5udh3OAWf/udlODtzxlJqfTuS9uB42ilRLCQoEBPGjJApIyLHpVKJv/ZZFhjRtSstr8B3v63Eqcz6Zzrp2T0Wwwf3R3Snjs02SOHs7ITBA/ogoX9vnD6jxdbEPdh74AhMl838aDAYzV0c59w6Fe054zPJ7ORpLb5ZsMLqfSe6U0fcfeuNLd6t8Ur69OyG6E4R+HnxHzh67KQ5bjSaMH/xajg7q9EtppNs+RHJxXJAr76uwURERERERES25LYZ1+PIsZN499PvRfGde/aj7+gbsXT+xxjYt6c8yVGbt2nrTpzJzhHFIjuEYsTg/jJlROSYNBbXvegNHA+Vymg0YeP+TKxtoFujxkmJATH+GBIbgEAfV8nHc1Yr0TfKD32j/JBdWIntqeeQfLIAdXpx19v03BK8vSARUwbHYHh8B07qTrLbvu9YvYWNrdGtsSGCIGBAn3h0iYrEb7+vwuGU46L1yzfsgFKpwIiBPVs9NyK5WRc3ciyUiIiImkdpaSm6d+8uy7FnzpwJrVYLb29vWY4vFYsbHYmUEzUm6xNQ9kHqySkJ+wsKaYduxb95j7gYJG9chFsfeA5/bhGfSPnul99xNDUNS7//ECFBAVe/M5Px6ts0uK/UE8dy/XvJ+TyTeGhJzzMJ+yqunnh6RiY2b0vE5Q/S1VWDmdNvgEqtbvqxJb+2pPzR5XwvlXJsnuhvfa3/76VUit8HDcYmvCfb7feF5ldUXIqPv1uEgvMlVuva+frg9plTENM5ssWOLwgCIiPCERkRjhFDBuHnRctwNq9AtI02Nw/vf/UrHrtnJoLtalZye32eyZm37b6PZ+Xk4cufl0GnF3c27dUjDnffeiPUKvl/knp4uOOBu2/Br0tWYtfeA+a40WjEN7+uwGP3zETniFAZMyRqfRoXexzQs933Qsdlp5/ZUi90kvKduN5j87lLRETUpgiC9O8jlmT7WiZ5AEPCoWX8DiXXOVLJ32ObJ41Wdw3jkgKA/77yFOJjozHnmddRfdlv2dxz+bhuyh349D8v4b7bZzTu0Iqmn7cSZD2XLmVcUtrzTNJwrIQxaJOMf29B0XBnsotSjp3Err2HIFz2B/Ly9MCMKROhUDu3ZHoNEoSr530lJin7C/qrb9NSJLyuoZIyfi3hGgtAWt5yaspY5EWNuN6gPioFcPmHnl5vgMlkusbCNyn/XhLfj+T8/Kjnb6QzGPHDhiM4lJ5f7+aj4oMxoU8YnNXS3lMaEtreDbcM64SpAzpg5Z4sbE85J85Pb8TSrceQU1iJW8b0hOIanzeS3gvl/D4qayGnnNdeSSHl2Fffd+/RU1i0dodVfPR1gzFt0ljZi2+9PD3wwN23YNX6v7D+r22idUvXboPG2RkDe3W12EvC+5Gcl23J+jzjdVvXTr7PPVeNRrRcXV0rUybUsuz0tSVr2hI+6+s7KSD1OnsiIjtUVlaGsLAwWY49Z84cWY7bXPipQdRG+Pp4Y81vX+L5R++1Wrd7/2H0HTMTO3bvlyEzaotKSkrx+8q1osFGQRAwY8okeMnYvYnIkbFzY/M5l1+E97/6td7CxuGDB+ClZx5u0cJGSxHhIXjhyQcxbuRQq4GR0vIKfPj1ApzJPttq+RBdVFenw3cLV6Kmtk4U798nHvfefpNNFDZepFQqcfvMKRieIJ4pXafX47uFK+2ksIuo+bi6igf0qqqqZb1QkIiIiIiIiKixbr1xEnas+gkdwoJF8bo6He5/5nU89Pxc1NXpZMqO2pr8gkKsWLtRFFMqFZg5dZLV+Rcikk6lsi5WMxgkFpe2UbU6A75adaDewsYAbw2emdodUwdGtFhh4+U0zirMGhqJxybHoZ2HdVF4UkoWvl+TDB3Hv0kGWWcL8NOKLVYlWpPHj7KJwsaLBEHADRNGY9r1Y63W/bpiE05reT0BtS0aF/HnSVU1x0KJiIioeXh6eqKkpETuNOwSixuJ2hClUol3XnkKC79+z2r2mbyCIoycfg+++GEhf6hRi9Lr9Vi8fBWqqqpF8VHXDUFkxw4yZUXk+CwH8/R6GWfGtWNFxaX4+PtFKCmrEMV9fbzx5IOzMWvaJLg4t/5My2q1GlMmjsFzj92PQH9xl8bK6hp8On8xcs8VNLA3UctYsXG7VRFwfLeuuGPmVCgUtvdTVKFQ4KapEzGgT09RvLSsAkvXbJYnKSKZuGrEnRtNJhPq6uoa2JqIiIiIiIjItvTq3hXJGxZh1NCBVuu++nExRk6fjbN5PF9KLau2thaLlq9GnU5cTDtxzAiEBAfKlBWRY1PWM/agN7Dg7VrV6Q34ctUBHNeeF8UFARjdMxgvzIhHRIBHq+cVE+KFF2f2xNA46/fQg2lnMX/NXhiMLGal1qPTG/Dzii1Wz7uJY67DhNHDbaaw8XJjrhuCGyaMEsWMRhN+Xr4JdTpeQ0Jth6uri1WspobdG4nIzgm8EdmCbt26ITU1Ve407JLtXVFKRC1u5pTxSFzzCyI7hIrier0ej/zfvzHn6df4Y41azMbN25B7Nk8U6xLdGYMH9pMpI6K2QaVk50apSssr8PF31oWNIUEBeO6xOYju3FGmzC6JCA/Bs4/dh8gIcVv7yuoafDJ/MQrr6TZJ1BJOZWZjS9I+Uaxjh1Dcc9sMKJUtP4tvUykUCtw+cwq6RIm7r+7afxRHT6TLlBVR69O4WA/osYMpERERERER2ZP27XywbuGXePqhu6zWJSYfRN8xM5GUfLD1E6M2wWQy4Y81G1FkcU6+V49Y9I7vJk9SRG2ASllfcSOL3a6FwWDE9+sPIy2nWBR3Vivw6PVxmDowAmqVfJcbOquVmDU0EnPGxUClFF+9fDj9HH7dcABGTuhOrWT99v3ILRC/VoYn9MeksSNkyqhxxo0chlHDE0SxvMJirN68S6aMiFqfZWMQAKjm9bJERETUDMaNG4e1a9fKnYZdYnEjURvVIy4GezYswtjrEqzWfb9gGa6bejdyLArQiKQyGAzYd/CwKNbO1wdTrx9nkzOWETkSq86NBs66dy2MRhO+X7jKqgtdRHgonnroHnh5tv7spA1x1Wjw2Jy7rIqzSssr8PWvy1nYSi2urk6Hn5etw+Vjx2qVCnfOmg61Wi1fYo2kVCpx+6xpVl1Yf12+gcVd1Ga4uloP6Fl2XiciIiIiIiKydSqVCu+9/hx++fy/0GjEE/mczSvAddPuxjc/L5UpO3JkZ7Q5OHbylCgWFOCPiWNGcEyUqAVZjocCnPD1Wi3dfgJHTxeKYq7OKjw+OQ4xIV4yZWUtvmM7PDwxFs5q8aWPe45lY03icZmyorYk62wB/tx5UBQLDQ7EjTeMt/nPekEQMHXiGHQICxHFNyceQEbWWZmyImpdGhdnq1hVNcdCiYiISLpZs2ZBq9Xi+HH+Nr1WLG4kasN8fbywZsEX+L/H7rVat+fAEfQdMxPbd+2rZ0+iplEoFHC2KBQICQ6ESz2dYYio+ZhMJlhOUGk0GmE0cqbSxtq6az/STmtFsZCgADx63x31FoDIzdnZCQ/cfSs6RYSL4tln87F+S5JMWVFbsXLjdhQUlYhik8ePQoB/e3kSagJfby/ceMN4Uay0rAK/r/1bpoyIWperxvr7eXUNi3uJiIiIiIjIPt164yTsXP0LIiwu4Nbp9Hjg2dfxwLNvoLa2TqbsyBGVlpZZxSaNHWEXk78R2TOV0rq40cDx0EZLPVOI7UeyRTEXJyUemxyLDv62M9HrRdEhXnhoQleoLTp2/rnnJDLPnpcpK2oLdHoDfl6xRdQlVKFQ4I5Z06BSqWTMrPGUSiXunDVN9L5pMpnwyx+bUKfjRNnk+Orr3MiJjomIiKi5zJkzB48//nirH3f69OmIjY1Fdnb21Te2QSxuJGrjlEol/vPyU1j0zftWP9ryCoow6sZ78fn8hTBZVsUQNYEgCOjTs7sodjT1BIqLS+RJiMgBncnKxsLfV+KL737GR198h3c+/Bxz/zcPX/3wq9W2ej1PSjdGQVExVmzYJor5envh0Tl32mRh40XOzk546N7bEBTgJ4qv35IEbS67M1PLKCktx5Zd+0WxyIgwjBw2SKaMmi6hf2/ExnQWxZL2HUFeQZFMGRG1HicnJ6uZhTmgR0RERERERPasZ7cuSP5zEUYNHWi17pufl2Dk9Nk4m1cgQ2bkiCIjwqG0KLZJOX5SpmyIHI/JZEJGdj52HT6FLcmp2LDzMFZu2YffN+2x2padGxunulaHBZtTRTG1SoGHJnRFWHt3mbK6us7BXrhvbAwUikvns00m4JcNB6Djvz21kO17U5BbUCyKTRg9HGEhQTJl1DRBgf6YNG6EKJZXWIy/kw7IlBFR61GrVVBaTIrAiV6JiIioucyZMwchISG4917rBmQtqby8HCaTCd7e3q163ObC4kYiAgDcdMM4JK39FZEdQkVxvV6PR1/4N+598hXU1NTKlB05koH9+kCtvjRTmdFoxM5dyS1yLKPRhIKiYpzJPotTmVocP5WJE+lncDorB9ln83iBNjmcqqpq/Pb7ShxPS0deQSFKSstQU1vbYIE6B/Ouzmg04eff16FOpxPF75g1DV6etjdDqSVXjQZ33XIjFIpLX/uNRiN+WrqG//7UInbsPQyj8dJ7jkqlwh0zp4meg/ZCEATcetMUuFh0nd6+56A8CRG1IkEQrAr4q6qqZcqGiIiIiIiIqHm08/XGuoVf4pmH77Zal7T3EPqOmYnEZF7MTdJ5eLijV/c4UWzvgSOorKySKSMix7Li7334edV2bNh5CFv3HsOuw2k4cCwTxzJyrLbVG9i5sTGW70hDSYX4mqDpgyLQKchTpowaL66DDyb2CRPFzp2vwNqk4zJlRI7MaDJhS3KKKBYaHIhxI4fKlJE0o4cPRgeL7ubb9hyBge+d5OAEQYCrxkUUq6ridYREZL8EhQBBoeBNIVz9j0XUSubPnw+j0Ygbb7wROTnW5yvImurqmxBRW9E9Nhp7NizCbQ89jw1/7xSt+2HhH0g5fgq///Q5QoMDZcqQHIGrqwZ9e8Ujac8+c+zA4aMYNnggPCUUCl0oZDyPLG02snLOISvnHLS5eaiprbvifn6+3ggLCUR4SCA6hAYiLCjA6uQFkb2oqKxETW3jCtFdNRpo+Fy/qq279uNUprhF+9BB/RATFSlTRtcuPDQY40YOxbpNW82xnHMFWL8lEdePts9BFrJNBoMBO5MPiWID+/ZEgH97mTKSztfbC0MG9cWmLZe+GyftO4LJY4bC2clJxsyIWp6rxkV0wV1VNYsbiYiIiIiIyP6pVCq8+9qz6NMjFvc+9SqqL5sI82xeAUZMm41P3n4J999zh4xZkiMYPLAv9h9OgdF4oThAp9dj194DGDV8sOT7NplMKCqpQNa588g6dx7ac+dRUFKOOp0BOr0eBqMJKqUCTmoVXJzUCPbzRnigr/nmyiuFyI4ZDEYcOnGm0ds7qfmEv5pjWUVITBVfaBkT4oUhsQEyZXTtxvQKwaHMImgLKs2xTXtPIb5zMCKCfGTMjBzNsXQtikrKRbGZ0yZBpbLP9xqlUolZ067H/z7+yhwrKavA0RMZiI/tLGNmRC1P4+KM8opLnxtskkBERETNbf78+Xj33XcxatQozJkzBw888ADc3d1b5Fjl5eXQarUQBPst8rXPX1VE1GJ8fbyw+tfP8co7n+Cdj78VrUs+eBR9R03Dku8/xtBB/WTKkBxBwoC+SN5/0Nw1zGAwInH3XowfM+Ka76uouAQ7dh9AUvJBlF12wqGxCs6XoOB8CfYfuTRrX1THcAwf2AvxsVFQKpXXfJ9EcvHx9oKT2gl1uisX9fr6eGPimBF2/SW2NdTU1mLVxu2imK+PN6ZNGitTRk03YfRwHEo5jtyzeebYhq27MLR/L3h5tsyPJWp7Dh07hdJy8WexI3xnHDqwH/7ammjugltdU4u9h49hcN94mTMjalmuGnHnxuqaxk2gQERERERERGQPbp42EV2jIzHtrieQqb1U0KHT6fHgc29g75Hj+OSdV+Hs7CxjlmTPfLy90COuCw4eSTXH9uw7hMED+sLF5dqfV7V1Ouw9fAIHUk7hTG4eqqqvfK5GpzegulaHUlQj73wZDpzIMq9r7+WGjsG+SOgWgaiw9hwvIruiUAhwcVKjpk531W07hgagvU/TJ1huC/QGI37bnCqKOasVuHV4J7t6b1AqBNwxojP+u/QwDMYL4zkmE7Bg40G8cMd1UNjRYyHbtm2v+PUSGhyIThHhMmXTPCLCQxARHorMrEuTPm/bc5jFjeTwLMdCWdxIRERELeG5557Dgw8+iK+++gr9+vVDQkKC+RYWFtZsxY7vvvuu+f9bqoCypbG4kYisKJVKvP3Sk+jdIxazH38JlVWXOnTkFxRh1LS78MG//4VH7r3drk5mku3w8HBHrx7dkLz/UnenfQcPY2jCALi5uV51f6PRhONpGdi2ax+OHEszFxs0l7TTWUg7nQUvD3cM7tcDQ/r1hLcXBz3I9qnVakR1ikDK8ZPmWIBfe0y7fhycnZ3h4uwMZ2cnKBQKGbO0H3sOpFp1f71j5tQmXXQgN5VKhTtnTcP/Pv7aPEu0wWDEzr2HMHGk9FmiiQBg2+6DouXIiDCEhQTJk0wz8mvvi9iYzkg5nmaObdt1AAl9evC7MDk0jYu4w3NVFTs3EhERERERkWOJj+uC5D8X4ZYHnsWmbbtE6779aRGOpp7A0h8+Q3CQ/XSuItsyZGBfHDp6zDyWWVtXhz37DmLY4AGNvo9zBeexPfkIdh88huqaK09u2ViFpZUoLK1E8jEtAnw9MCw+EgPiwqFxVjfL/RO1JEEQENMxWNS90UmtQs8uEXBWq+DirIaziyu8PVwREeLP8/hXcTA9H+fLxcUcUwdGoJ2nSwN72K5gXzdM6BOG1cmXirlzC8twIqsAXTv4y5gZOYrC4jKkpGWJYsMS+jvE+8ywhP6i4sbj6VnIKyxGQHt2PiXH5eoq/qyrrmFxIxEREbWMpKQklJWVwcPDA4mJiUhMTLTaxtPTs0n3XVZWJjU9m8Er24moQTMmj0XS2gXoFBEmiuv1ejz+wpsYOeUOpJ44JVN2ZO8GD+wnKrDS6fRI2rP3ivuYTCbsPZiCN977HJ9+/xsOp55sVGGjSqmExsUFnh7ucHdzhbOTU6NOLpaWV2Dt5kS8/O4X+O63FSgudZwvAOS44rpEi5YLis7D08MDPt5e0GhcWNjYSCaTCdt2HxDFesR1QUxUpEwZSRceGowBfXuKYjv2HITBYJQnIXIo+UXFOJlhPZjnKCwfizY3D9rcvAa2JnIMlgN6VdUsbiQiIiIiIiLH087XG2t/+xLPPjLbat2uvQfRZ+QULFy2utkn2qS2oX27CxOnXS4p+QDq6q5epHj0xGnMm78Mb37yC7bsOtRshY2W8s6XY8nfh/Dil2uxYON+5BdXtMhxiJpTbGSIaLlOp8fgnlEYOSAOCT2j0ScuEp3CA6FUclz0arYf0YqWOwZ4YEis/Rb1j+kVgkAfcSeu7QdPy5QNOZqd+4/j8m+ELi7O6Nerh2z5NKc+8XFwcxW/dnYkH5YpG6LWobGY2JydG4mIiKi5/fnnnxgwYACeeOIJLFmyBOXl5TCZTPXeSktLm3S7/D7sHTs30gVSZxCS9GKQcGzJEx9JOJFpkloIIOFv1opvPt26RmHPhoW49cHnseHvnaJ1WxP3oOfwG/D0Q3fhlacfvGrHPZMg7cSxoJDwlmUySDmyhH0BSf/WcmrhmcW8vb3Qo1tXHDycYo7t2XcQgwf2g0Zj/VwqLa/AwuVrcSjlxBXv19PDHeGhwaKbt5f1bAZ6vR655/KRlZ1rvuWezYPeYP1cMRpN2HfkOFLTTmPGpJEY2Lt7A8WREv5mUt9TJP172elzVPqHgIxa7t+rc2Q41CoVdHo9AMBoNOJ42in0ju8m4ZgXtZ3nWXpmNnLzCkUxRyjUGp7QH0l79puXS8oqcOT4KfSMi77CXmT75H99nTotHvx2d3NFrx5xMmXT/OK6RMHXxxvni0vMsbTTWQgPCZQvKWocWX/r2sdvPmsX/mauGvEgdnVNLRr3XUCm8wMkA3v995L/c5OIiIiIiGyLSqXC/159Br27d8W9T72K6ssuas3LL8Stc57Edz8vxqf/e92uJ8AjeQxL6I+U42nm5eqaGuw9cASD+tR//rS8ogqL1mzBgZSrTzLs7uaK8NBghIUGIyw4EK6uGqjVaigEAQaDATq9HueLS5GVnQttdi6yz+ZB/8/4kaU6vQE7D2diT2oWJg+Ow4jenaFQ2Otvf3J0kaH+cHFSo6ZOZ44dy8jFgB6dr7AXWcopLEd6bokoNqJ7kF13oVMqBAzvFoRF2zPMsSMZ51BcXg0fD80V9iS6uiNpZ0TLA/v2grOzk0zZNC+1Wo1B/Xtj05ZL1wYePp6BGycMlzEropZlORbK4kYiIiJqTu+//z6+/fZbc9Hhxd/aLfmb294LHFncSERX5ePthdW/fo5X3vkE73z8rWidXq/H/z75DguXr8W8t17EDeNH2PWJTmpdgwf2ExU31tXpcCItHT17dDfHTCYT9h5KweIV61FZVX+3GBdnZwzoE4+hg/ohOKhxswiqVCpz8eNFOp0OB46kYlviHmRkaq32qa6pxc+/r8P+oydw29Tx8PbyaOxDJWo1Tk5OiOoUIeqsm3LsZDMVN7Ydll0b/dr7oosDXLQSHhqMiPBQZGZlm2Pbdu9ncSNJdibnnGg5qlNHqFWO83NToVCgS1QkEi8rDs7KYedGcmwaF4vOjQ18FyciIiIiIqkkjqnINSQjeZDcTseS7PXvLefYnVwXVDThId88bQK6Rkdi+t1P4HRWjmjdX9sS0WPoRDz36L148Yn74ep6hQIJKRO+Spws1m4nHZLwuAVIm0C0pS/6CfD3Q0znSJw4danQZtfeAxjYN95qXH3fkRNYvOovVDRwHkYQBMR1icKAPj0RER4KXx+vRo3NDx7QBwBgMBiQey4faemnsX3XXuTlF1ptq9MbsWzrERxMy8XtE/oiwNd6PFRQOVvFGkvSX9tYf2HmNdxB03eVNBGzjJPcCkppx5aSu5S8r3JclVJAdEQgDp/IMsdS07MxoHszjOVJeU8wSpl0G5D0HG3C33v70WzRsqerGj06+jY9BxvRL8oPf+zKRK3uwt/TZAJ2Hj6DyUMsisoVUj5zpX5eS/m3lvC6lvqdsA1fD1Zbp8O5whJRrE9Px7oGo2/P7qLixsLzpaiqqoGr5ho/9yU/Tdru80weTf/ck/w9WrbfihcnehWPhVbX1Dbufc6+awbILsj5Pijl2Hz/lpUg8J8AaNPfV+1dYmIitFotsrKyoNVq4e3tjbCwMMTFxSEhIUHu9K7Z4sWL8c0330AQBAiC4DCdFVua41xtSkQtSqlU4u2XnsTgAb3x6Av/xhltrmh9VvZZTLvrMVw/djjmvfUiOnYIlSlTsicn0tKtYorLTiCXV1RiwbI1DXZrDA4KwLBB/dG/dw+4uDR9EO0itVqN/r3j0b93PLQ5Z7E9KRl79h1CnU4n2i7lRAbenPcdbrp+NAb2dqyTleQYYmOiRMWNp7OyUVVVfeWLLcisvKIKB1JOimLDBvUTvT/Zs2EJ/UXFjcdPnUF+4Xn4t7f/wUqSjzZXXOgXHhokUyYtJzw02KK48dwVtiayf5bfGzhbKREREREREbUF8XEx2LNhIWY/8QpW/7lFtE6n0+PtD7/Cgt9X4+O3X8L1Y6+TJUeyPxqLC6fLyiug0+ng5HSh21N5RRUWrfrLamziIjdXVwwe0BtDBvZD+3Y+Tc5DqVQiLCQIYSFBGDF0EE6eOo1tiXtwKOU4jEZxoU1GbhH+8+NGTB7SDSP6RLGLI9mc2MgQUXGj9lwRyiur4eHG8dDGqK7VY8/xs6JYQtcAqJT2Px7q4qRE/2h/bE+5NI6z80gmJgzq6hCPj+SRnVckuhhZEASEBQfKmFHzCw70h0qphN5wqVBbezYfMZFhMmZF1HKsJnqt5kSvRERke1JSUrBo0SIcPXoUWq0WZWVlAABPT09069YN48ePx6xZs2TOsmkuPrZFixZdcTtPT0/cf//9mDNnTitlJt17771nLmr09PTE+PHjMXjwYMTGxsLb2xseHs3bXEmr1eLVV19FUlJSs95va2NxIxFdk0ljhmPE4P5466Ov8d5n30OnE8+OuPrPrdi0bRdeeuoBPPvwbDg7O8mUKdm6vPwC/L1tpyjm6+ONrjFRAICi4hJ8/M2vKCg6b7Wvu5sbbp4+Cb16xLVYp9CwkCDcOuMG3DBhNJauXIc9+w6J1lfX1OKnpWtQUHQe148eyo6lZFOiO3eESqWEXn/hpLPRaMSJUxno1SPuKnsSAJzMyILBcGkQX61WY1C/3jJm1Lz6xMfh95XrRN1wj53KZHEjNZnBYED2uQJR7PLOyI7C8jHlF51HTW0tXJylT7BAZIusZytlcSMRERERERG1De18vbHi58+wfM0mPPnyf5CdK57kKjMrBzfc/jCmThiFD//9AjqEhciUKdmDk6cycPBIqijWuWMHc2FjzrkCfPbj7ygtr7Ta183VFVMnjUH/3j2gVqubNS9BEBATFYmYqEiUlJZhw+bt2Lpzt2gbnd6IZVsO40RWPu67YSCc1LzEiGxHp1B/ODupUVt3YaJik8mEYxm56N+9k8yZ2YdjZwpRp7tUwKQQgMFdA2TMqHkNjQsQFTeWV9XiVHYhunTwlzErsmdZZ8XdjoMC/Myf5Y5CpVIhOCgAWdmXGh5k5bK4kRyX5VgoJ3olIiJbkpiYiPfeew8pKSkAgISEBEyYMAFhYWEoLS1FSkoK1q1bh8TERLz66quYNWsWnn32WXh6esqc+dWVlZXh5ZdfxoYNG8yxWbNmYfz48ejWrRs8PT1RVlYGrVaLtWvXYvHixXjvvfewbt06LFu2TMbMG2fx4sUoKyuDIAiYO3cuZs6c2eLHDAsLw7x589C/f38AQEVFBdzd3Vv8uM2N0xER0TVzddXgrRefwKEtyzFiSH+r9TU1tXjlPx8j/rpp+GvbLhkyJFtnMBiwfNU6UfGQIAiYNnk81Go1zuUX4v0vfqy3sLF3fDe88tyj6B3frVUKCt3dXHH3LTfiwdm3wtPD+oN+3d9JWLxqE4xGtosm2+Hk5ISoyI6iWOrxNJmysT9ZFheqREV2cKiul2q1GrFdokQxdqAjKc7mF5mLqS8KC3G84saQoABRB1eTCdDm5suYEVHLspqttIqzlRIREREREVHbIQgCpl8/Bqk7VuHZR+6BSmVd1PXHur8QN/QG/Pfjb1FXVydDlmTrqqqqsXLdJlHMSa3GpHEjAQAZWbn48NtF9RY29uwei1eeexSDB/Rp9sJGS95enpg1bRKeeuge+LWznggxJeMcPl26A9W1uhbNg+haqFRKREeIu6alZuQ2sDVZOpNfKlqODvGCj7vjTOYY7OuGMD83USzrXLFM2ZAjyMq1nOjVMSe3sJzsNSs3T6ZMiFqexpXFjUREZJsef/xxzJ49GykpKYiLi8OyZcswf/58zJkzx9ypce7cuUhOTsazzz4LAFi0aBFGjRplLoa0VYmJiRg1apS5sHHWrFlITk7G3LlzkZCQYC7O9PT0RFxcHJ577jkkJydj1qxZSElJwezZs+VMv1F27twJQRAwb968VilsvMjT09MuiluvhMWNRNRkXaIisen37/HLF/9FgF87q/Un0zMxZsa9uO3B53A2r6Cee6C2auuOJJyzeE4MHtgXYaEhyC88j4++/hklpWWi9e5ubrjvjlm4746Z8HAXn4RuDT3iuuCV5x5F/97xVuu27tqPJas3wWRigSPZjtiYzqLljDNZqOaJuEbJyhGfoG8LHei0HJQgCbLPigv82vn6wN3NVaZsWo5arUZwoHhGX752yJFZFvZzQI+IiIiIiIjaInd3N/zvtWex/6/fMXRgH6v1VVXV+Ne/P0CvkdOxZeceGTIkW7Z249+oqKwSxcaNGgYfby+kn8nBJz8sRXVNrWi9m6sr7rn9Jsy5c1a9E6+2pKhOEXjx6Ydx3ZCBVuvSswvx8eJtLHAkmxIbKS4uyjpbiIoqnsdsjKw88fUYEQEeMmXSciL8xY/pTB6LG6nptOfEnRsd8RoCoL7iRk70So7LVSMeC62u4XcIIiKS3+zZs82FfwkJCVi2bBni4uIa3H7OnDnmboZlZWWYPn06EhMTWyXXa/XNN99g9uzZKCu78Ht03rx5mDt3bqMK8ubOnYtx48YhMTHRZh/fRVqtFgkJCRg7dmyrH9vLy6vVj9mcWNxIRJIIgoBbb7wex5PW4NH7bhN1s7not2Vr0WXQJHz81Y/Q6/UyZEm2JDsnF9sTxYO7Af7tcd3QBJSUluGTb39FWXmFaH1woD9efPoh9I5v+Ataa3BzdcXdt96IW2fcYNU1cuuu/Vi9abtMmRFZi+7cESqV0rxsMBhx4lSGjBnZB5PJZFWs5IgDE+EWXfVy8wpRp+MFCdQ0lRYFT37tfGTKpOVZzlrOYi9yZK4a8WylHNAjIiIiIiKitqxb1yhsWfETfvjkP/Brb93Z7tjJDIycdjfufPj/kJfPSV8JSDl2EkePnRTFoiIj0Du+G7S5efj8p2WoqxOfl+8c2QGvPvco+vbsbjUW2VqcnZ0wc+pEPPngbKsLvrPOFePLZTtRpzPIkhuRpU6h/nBSX+qsazKZcIzdG6/KaDJBW1AuioW3b91i6tYQbtG5UZtXIk8i5BCKSy2uYwryb2BL+xYSFCBaLrF43ESOROMi7lhczQkSiIhIZo8//ri5cC8uLg7z589v1H5xcXGYN2+eeXn27NnQarUtkmNTLVq0CO+99555edmyZRg/fnyj9x09erS56HPhwoUtkmNzyc7ObvRja24eHh4wmUwoKSmR5fhSsbiRiJqFl6cHPn77Rez5cxH69+5utb68ohJPvvgm+o+ehl3JB2TIkGyBTqfD8lXrRR0OFQoFpk2eABOAz+cvQlFxqWifiLAQPPXwPfD2sp1WyUMG9sU9t91kVcy77u8kJO07LFNWRGLOzs7o3DFCFEs9niZPMnakqLjUqljJEYsbw0KCRBdGGI1G5JzjBTfUNDqdePIKtVotUyYtz/Kx6ThxBzkwjYu4uLGqqlqmTIiIiIiIiIhsgyAIuHPWFBxPXIMH77653uKzX5auQkz/0fjs259gMLAArK0qL6/A6g2bRTGNiwtumDAa50vK8Mn8JaiprROt79Y1Go/OuRMerdytsSHRnTviqYfvseoeeSq7EN+vTobxsvFeIrmo1SpERwSJYqkZOTJlYz8KS6tQUyce3wizKAR0BGEWBZtFZVWoqK5tYGuiK6uzGBO0HENxFC7O4mIvvcEAo9EoUzZELctyoldObExEdk0QeLt4s1OLFi0yF+8BEBUrNsb48eMxbtw48/Ls2bObLTepFi1ahFdffdW8/Oyzz16xG+Xl1q9fj1dffVVUrJmUlNTsOTansrIyhIaGynLsZcuWITk5WbbjS8XiRiJqVr17xCJx7QJ88e5r8PG2LkY7eOQYEsbfhPufeglF54tlyJDk9NeWHVb/7tcNHYTAAH+s+2s7ss+Ku6WFhQThsfvvgpura2um2Sh9enbDPbfNsBq4XrL6L5wvKZMpKyKx2JjOouX0zDOoqeGAzZVk5ZwTLbu7ucHH275btdfHxcUZ/n7tRDFtTl4DWxNdmWWBn1qlamBL+6dWix+bZWEnkSNxdRXPzF9VzeJGIiIiIiIiIgDw8fbC5/97FbvWL0SfeOsLcUrLyvHo869jwJhpSN5/SIYMSU4mkwmr1v+F6hrxxdETx1wHNzc3/LR0LSoqq0TrusfG4IG7b4GTjU0cFxIUgKcfvteqwPFIxjnsOHRapqyIxGIjxZOUnsktRCU7L11RVr74egYPjRrebk4yZdNygnw0UCnF13OweyM1hcFohNEoLuq3HDN0FPVNYqvXc8IOckyWXcotv78TERG1psu7Go4bNw5hYWHXfB8PPPCA+f+1Wi2++eabZslNCq1WKypsTEhIwJw5cxq9/9q1a61iZWW2fY2+p6cnvL29ZTu+h4eHbMeWisWNRNTsFAoFHrhrJo7tXI27b55a7zbf/rQIXQeOxfxfl3KGpzaitLQMu/eKu3aGBAdiyKD+OJOdiz+3JIrW+fu1w6P33QGNxnZnO+sd3w23zrhBFKuprcOvy9eJulMSySW6cySUyktf9wwGI06kpcuYke0rLa8ULQcGtK939m1HEOjvJ1ouq6iQKROydwpB/LPSkWfstvzeqlA45vsDEVDfgB4nSCAiIiIiIiK6XL9e3bFr/UJ88p+X4eVpfdHIvoNHMWDMdDz87CsoLimVIUOSw7ETp3AyXVz4F9clCt1iY7B99wGkndaK1kV36oj77pgJpVLZmmk2mr9fOzw6506rMdvl21JQWFrZwF5EradzWACcLisyMplMOMbujVdUUCIusA5t7+aQ46FKpQLBvuKJtAtKOB5KTVDP0KcAx3vNAKj3vcCRx36pbdO4iDuVsnMjERHJ5d133xUV7N18881Nup+4uDhRUeTXX38tOTepnnjiCdHy3Llzr2n/+oo8m1L42ZpCQ0NFnSap8VjcSEQtxt+vHb7/+C1sXfkTunWNslpfWFSMex9/AcOvvwWHU47LkCG1ppLSMquCv8ED+8FgNOKnRStFs5wpFArcd8cseFjMAmqLBg/og4F9e4lix9Iykbj3sEwZEV3i4uKMzh0jRLHUE6fkScZOWHagc3ZyvFlKL7J8bDrOuEhNpFaLL7rR6XQyZdLy2lKXSiLL17Jez06lRERERERERJaUSiUeufdWHNu5GrfPmGy13mQy4Yvvf0VM/9H4aeEyTo7ZBpw4lSFadnZywqSxI1FUXIrl67eI1vn6eOOB2bfU2yXJloQGB+Le22eKYnU6A37ZcIAFDyQ7tVqFzuGBolhqerZM2dgHnV48kaOrs+OOdbg6i99f6/ScfJ2unUIhWBX9WY4ZOor6xoI4HkqOyslJ/BnBzo1ERCSXxYsXi5YTEhKafF/jxo0z/39ZWRnWr1/f5PuSatGiRUhJSTEvJyQkXHNhYn2FnrNmzZKcW0tKSEjAzp075U7DLrG4kYha3NCBfbBv0xK8O/cFuLm5Wq3fuXsf+oyYgmdeeRvl5ZwlzVEFBwVY/fvv3X8I6/7ajrP5haL4hNHDERosHoCwZTOmjIe3l6co9vvazThfYtutr6ltiI3pLFo+dToTtbXsvNQQvU58sl7lwCfq1WrxY9PpHHMAhlqeWiU+6V/jwN3dLB+b5euIyJEcPXZStNy5YweZMiEiIiIiIiKyfYEBfvjp8/9i8/If0DW6s9X6gsIi3PXws7hu8i1IsfjNTY4lwK+9aLm2rg4lpeX4+fd1qLM4D3/7zCnQuIg7Itqq2JjOGDKwryiWpi3EjkOnG9iDqPXEdQoRLZ/JLUBVteOOVUhlOeGpk8pxLx+0fGyc7JWaQhAEqFVtY7LXOovHpVAIUCod9z2C2rb0THFHJb92vjJlQkREbVliYqKoa2NcXJyk++vevbtoedGiRZLur6nKysrw6quvimJz5sy55vsJCwvDpk2bMGvWLMyaNQvz5s1r0v20pgkTJlgVrFLj8JdHvQSZbnZMEJp+k3ZgaTcpeUu+KSTc5Pp7AzAZm3RTq5R45sE7cWznasyYPNbqbg0GAz78/Ht0HTgWS5avhElfCxjqRDeTUSfhZpBw08t2a+rf23yT9Fxp3vcztVqNIQP7iWKHU49j6coNolhocCDGjxom4Una+lw1Gtw64wZRrKa2DkvXbJb4Wr+GW5tkknhrG2KiOolONhsMRpxIy7jCHlci49/bZGqVW9ueNVvqa6q5blIegsTngIxM/zz/7PHm6+0heiy55/JhNDrmzLc5Z/NEy77enrCb10dbZcfvC3I7nHpctNwjrotMmRARERERERHZj+sG98fBbavxzmvPw9VVY7V+W+Ie9Bx+PZ5/7R1UVFTKkCG1tN7x3eDi7CyKffXjIqSdFl88PWRgX3SJ6tSaqUk27fqx8PX2EsWWb0vB+bIqmTIiuqBzeICos5jRaMTx0zkyZmTbDEbxuW9Fc1xLZKOUCvFjMxgcc/yKWp6bRvzZnl94XqZMWlZBYZFo2VVjH5MwEDXFkVTxpDM94mJkyoSISDpBUPD2z83eLFy4ULTcrVs3SfdnWRxpWTzZWiyLKj09PZvckTIsLAxz587F3LlzMX78+OZIr0XFxcVh4MCBeP3111v92EuWLMH777+Pigr7bDZmf69gIrJrocGBWPzdR1i78Ct0irBuLZx7Lh+z7nsaE2bdj7SMzNZPkFpU397x8PBwMy+fzspBdna2uZhIoVDgzpunQ6lUNnQXNqtb12gM7NtLFDuUegJFxSXyJET0DxcXZ0R2CBfFUk+ckikb22fZhU2vd9xuhpadGtmBjpoqLDhAtFxdU4PComKZsmk55eUVKC4pFcXCQ+yn0zTRtTAYDFadG1ncSEREREREIrJOICrjJKByTZJrz38zuY4tdcIjo77JN7USeP7Re5CyYzWmTBhllZper8e7n3yNrgPH4PcVq2HU18JkqIPJUAeYDBJv8jxmSJ60VcrkVNJeX819wZyLizMS+ve+7Klowl87dokuKvL18cb068dJe47LQOPigttmThHF6nQGbDmUDTh5NOomqJybfIOglHZTKCTc1BJuKmk3OT8/FMqm3yT9e13bRMBOTk7o3CFQ9PmRciq7VV4XYkaJt9ahsujCpnfggj+dxWOz7L7XZJLeTxTS3hOkvhe24mvTkSbsDg1oJ1rWZufKlEnLyrJ4XGGBfrjmyVqlfg+X9L1QvklmpUwmLC1vaTdpeUv+q8lz++f5ctiyuLFrVCOfZ0RERM0nKSlJtBwWZl1bcS3q23/dunWS7rMpLIsbJ0yY0Oo5yGnevHlYu3YtNm7c2KrH/e233/Dtt9+ipKSkVY/bXOz/lyMR2aXxI4fiyLaVePXZh+Hs7GS1/s8tO9F92BS89t9PUF1dI0OG1BLUajWGDhoAANAbDNDmnEN5eTnKy8sBACOGDkRosP0WCcyYMl40E6vJBOzYc1C+hIj+Edc1WrR86nQmamtrZcrGtqnVatFybV2dTJm0vDqdTrR8+Yy2RNfC29MdHm6uopjlwJcjsHxMTmo1Av18ZcqGqGWlpWeipkb8XYHFjURERERERETXpkNYCJb/9BlW/PI5IsJDrNZn557DjNmPY/KtDyIjU1vPPZC9GtC3p7nTUX7hedTW1iE399L5xVtn3AAXF+eGdrdpXaM7Y9BlxZsAkHT4FOp0jjtZJNmH2Ejx+2xmTj6qqjkeWh+1Sny5oGUBoCPR6S2LG3mpJDVNWJCfaNkRx0KBeoobg/1lyoSoZRWdL0HO2TxRLJ6dG4mIqJVptVqrropSixuBC10SL5eSkiL5Pq9FYmIitFrxuU7LjpKOztPTE/Pnz8dLL72EJUuWtNpxL9ZjeHt7t9oxmxN/sRORbFxcnPH684/i8NY/MPa6wVbr6+p0ePP9L9Bj+BSs+2ubDBlSS+jdszu8PD2QlnHGPItTdvaFWROvGzxAztQkc9VoMKBvT1Fs26790Dlw5zeyDzGdI6FQXPrap9cbkJaeKV9CNszL3U20nJdf2Ewzztmec3kFomUPi8dO1FiCICAsRNy90REH9LJyxI8pNMhf9N5K5EgOpxwXLQcF+sOvfbsGtiYiIiIiIiKiK5k8biSObl+NF5960GqCPQBYu2krug29Hm++/zknJnQQzs7OGDygLwDg+KkzAIDS0lKUl5cjLCQIXaM7yZmeZGNHDBEtV9XUYf+xMzJlQ3RBdEQQVMpLXfmMRiNOZDreWEVzcHEST3haUuG4k72WVIofm4uT9ecwUWOEB7UXLWtzz8JodKzCYJPJhKzss6JYOIsbyUFZdm10dnZCVGQHmbIhIqK2KjEx0SpmWZjYFF5eXqLlo0ePSr7Pa7Fw4UKrWEJCQqvmYAvi4uLw+++/4+uvv8a9996LioqKFj9maWlpix+jJfFKTCKSXVRkBNYt+hqLv/sQIUEBVuvTM7WYdMuDuOnuR6HNOVvPPZA9UalUGJLQH5naSwMJlZWV8G/ng3a+PjJm1jyGDeonWq6uqcXeg6kyZUN0gUbjgsgO4hldUo6nyZSNbbOcebC8ohLFpWUNbG2/ampqkVdQKIqFh1h/BhM1Vniw+PlzRpsjUyYt54xWfBEEXzPkyA6niosbe8SyayMRERERERGRFK6uGvz7xSdxeNsKjBo2yGp9TU0tXnvnY3QfMhEb/94hQ4bU3Pr3iUd1TR0qqqrNsZycHAxL6A9BEGTMTLoAv/boYlGg+eeu1r1QjsiSk1qFzuGBolhqerZM2di24HbuouXsokoYjY432WtNnQH5JdWiWHB76RcKU9sUbtG5sba2DvmFRTJl0zKKzpegqlr8mmFxIzmqw6knRMtxMZ2hUqka2JqIiKhl1NdRsTk6N1reR2t3bkxKSrKKNcfjskdhYWHYuHEjQkJC0K9fPzz55JP1/n2aQ1JSklUnUHvDb2NEZBMEQcCMyeMwbsQQvPHuZ5j39c8wGAyibX5ftQHrN2/Ha88/hiceuKvemU3JPpRV1kCtVotnnzXqYTKZ7H4wLyjQH9GdOuJk+mlzbO3mHRjUt4eMWREBsV2icOr0pVlz0zJOo66uDk5OTjJmZXva+3pD4+KM6ppL709Z2bnw9fa6wl72Jzv3rKgjpUIhICTQ7wp7EF2ZZaHfqdNnUFxSCh8Hee1UVFbh2IlTolh4SGADWxPZP8vOjT3iWNxIRERERERE1BxiOkfiz6XfY9Efa/H0y+/gXH6BaH1aeibG3ngXZk6diA/+/RJCgnkOyl6p1WqU14g7hlVXV6O9r2OcMx2W0B/HT6abl/OKypCZU4CIEI41kHxiO4Xg+OlLky+ezs5DdU0dNC4cD71cuL+4wK9Ob0ReSTWCfF1lyqhlZBdW4PKSTYVCQIifY7wHU+vz8nCFl4crSsurzLHU42kI9Hecz73UE+IJst1cXeDr7SFTNkQt61CKuLixR2y0TJkQEVFbptVqrWKWXRebwsPD+jucVqttlQJDrVZrVWAXFxd31f3KysqapWulLTh27JhV7JZbbkG3bt3wzTff4J577gEAxMbGolu3bvD09IS3t3eTj1dSUoKysjIsXrzYHHN3d7/CHraLxY1EZFM83N3w3hvP465ZU/Hw83Oxc89+0frKyio8/9p/8eNvy/D5e29gqEWXPLIP23ftR3BwME6fvlAAqNG4wGQy4sTJU+gSEyVzdtINS+gvKm4sKi5FSVkFvD3t88sCOYYuUZ2wesNmGI1GAIBeb0BaeibiuvIE3eUEQUB4cABOZGSZY9rsXPTs1lXGrJpfVra4A12Qf3s4cdIAkiAmMhzOTmrU1ukAACaTCTt27cXk8aNkzqx57Eo+AJ1eb15WKhWIjeooY0ZELevQUfGJtvhuLG4kIiIiIiIiai6CIODmaZMwYdQwvPrOx/jsu1/N5+4vWvzHWqzdtBVzX3gSj91/J7uI2KGa2loYoYKTkxPq6i4UOYYFB2Jn4m5Ed+5k9xO+du8aDW8vT5SUXrpgbV3iUTx00wgZs6K2LrpDIFRKJfT/vKUaDEacyMxFzy4RsuZlazxcneHj7oLiihpz7ExBhcMVN2YVVIqWg9t5Qq1SypQNOYKo8CDsTblU2L8tKRkjhg6y+8904MLY7rakZFEsKiLEIR4bUX2OHDspWu4RGyNTJkREzUQQAH5sX/g72JH6ihtb8litUdyYmJhoFauvYFOr1WLhwoXYsGGD6O8QFhaGhIQEjB8/HgkJCS2aa0u56667UF5e3uD6i01JUlJSkJqa2mzHvbzZib1SyJ0AEVF9usdGY+vKn/DdvH+jna+31fqU42kYfv2tmP3I88gvKGr9BKnJSsvKkV94Hu3atYOLiwsAICQoAIIg4O9tOxziwzW+Wxc4O4tnf0zae0imbIgucHXVoGN4qCiWcvxkA1u3bWEWHegsCwEdQVb2WdEyO9CRVC7OTujfM1YU27l7H/SXFQTaK6PRiO0Wg3m94mLg6eEmU0ZELauktMzqs4+dG4mIiIiIiIian5enB+a9/RKSNy7FgD7xVusrKirx9Mtvoc+IKUjcvU+GDEmKrbsOQKFQICgoyBwLCw1GVnYO0jNOX2FP+6BUKpHQv7colq7NkykbogucndToFCYe50tNz5YpG9sWZtG9UVtQIVMmLSfL4jGFB3jLkwg5jMG9xRMi5xcU4cSpDJmyaV7pmVnIPSv+HB/St5tM2RC1LL1ej5QT6aIYOzcSEZEcWrO40bKbYkvZuXOnVcyyk+S7776L0aNHY8OGDRg3bhzmzp2LefPm4b777kNYWBgWLVqE2bNnY/To0fUWS9o6Ly8vmEymem/AhYnvLt4uamj7a7k5wsQkLG4kIpulUCgw+5bpOJ64FnPuuKnebX5cuBxdB47DVz/8ZjWjKdmmHbsPALjw4RwcHAwA8P6nlXRefgFSj52QLbfmolQqERYcJIrtOZgiUzZEl8R2EXdGTUvPNM8WTJd0sCj0O5meiarqapmyaX46nQ6pJ9JEsfDggAa2Jmq8of3FF2CVlVfg0NHjMmXTfI6npaOg6LwoNmxgL5myIWp5R1LFr1u1Wo2YzpEyZUNERERERETk+Hr1iMXOtb/hqw/mwsfbeibzwynHMXjCTNz3+L9QaHGeimyTyWTCjj0HAQDt27eHk5MT1Go13N0udEX7e9tOh5jwtXNkB9Fyda0O54pKZcqG6ILYTiGi5QztOdTUcjzUUrhFcWNabplDvC9dZDSakH5OfPFuWICPTNmQo4jqEITA9t6i2LbE5Po3tjPbEveIlv18vRATGS5TNkQtKy3jDGpqakUxFjcSEZEj8fb2toq1ViFlfZ0IL89n9uzZWLx4MebNm4dNmzbhueeew6xZszB+/Hg899xzmD9/PpYtW4awsDBotVrMnj0bs2fPbpXcm8vFDpnXUsRIF6jkToCI6Gra+Xrjq/ffwOzbbsLDz72Gg0eOidYXl5TioWdexfxfl+Lz9+aid3ycTJnS1RgMRmxLujSzrK+vL86ePQsvz0uzMmzZvhNdu0RDobDv+vvw0GCcOn3GvJxXUITzJWXw9fa8wl5ELatrdGes+fNvczG4Tq/HqYwzVkWPbV10xzAoFIpLfyedDruSD2DkMPtsc29p/+FUVFRWiWJdoiLkSYYcSkigHzp1CEH6mRxzbMvOXegdH2fXMwNt2bFbtBz8z+MkclSWRcmxMZ3h5OTUwNZERERERERE1BwUCgXm3DET064fjxfeeBff/7rEapvvflmM5Wv+xH9fex733H6T3Y+lObJM7VkUFV8oqlEoFAgODkZFean5PGlO7lmcTEtHTHRnOdOULDwk2Cr2Z9JR3Hn9YBmyIbogukMglEoFDIYL43wGgxEnTucivkuEvInZmI5B4mL63PNVyMyvQMcAjwb2sC+p2mIUV4iLWjsG+8qUDTkKQRAwtG8slqy/1D3mcMpxFJeU1jtBhb0oK6/AgcPii9CH9usOhcJ+x3eJruRw6knRcnCgP9q3YwE8ERG1roY6KXp6tsw15iUlJS1yv5ZKS60nvbrYufHdd9/F0aNHzcWLDYmLi8OmTZswffp0pKSkIDExEdOnT8cPP/zQYn+f5nR5p0qTyYTY2Fh069YNnp6e9RaeSlVSUoKysjIsXry42e+7tbG4kYjsxsB+vbBn0zJ88f0CvPL2hygrrxCt37P/MPqPno6H7rkNb774JLy9bP8DrK3R5p5FeWWleVkQBERHdYaLi7M5VlBYhKMpx9Cju30XqYaFWg/mJSYfxPVjhsmQDdEFrq4aRISHICPz0iwsR4+dQHTnjlAoFLwQ4h8e7m7oGReF/UcudZLdlpiM64YMdIi/keWsizGR4Qhoz8E8ah7DBvQUFTemn87Cnv2HMKBPT/mSkuBwynEcPSYe3Bg2oKddF2sSXc3hFHFxY4+4LjJlQkRERERERNT2+LVvh+8+eQf33DYDDz37Ko6knhCtP19cgjlPvojvflmCL96fi57dY2XKlK5kz8EU0XK7du3g7eEqiv29bQeiozrZ9blGV1cN2rfzQWFRsTl24HgWbhk/EGqVUsbMqC1zcXZCp/BAnDyda46lZmSzuNFCVIgv2nm6oKisxhzbnnLOYYobt6ecEy2H+Xsh1M9+i8/IdgzoEY0Vf+1BnU4PADAajVi9YTPumDVN5syabs2ff8NgMJiX1SolBvbid0xyXJbFjezaSEREbUF5eXmrHKe+ok1vb2+kpKTg22+/vWph4+V++OEHjBo1CmVlZUhJScH06dOxadOm5k652V3++ObMmYNnnnmmVY47fvx43HPPPQCAiooKuLu7t8pxmxOLG4nIrqhUKjx2/52YccN4PPvqf/Db76tF641GIz779mcsXbkO7819AbfOuMGuB4QczZnss1axXvHdoYQB5/LyzbEt23eiW1xXuy4iCq+nuDHlZAaLG0l2sTFRouLG1BOnkHriUwAXCo6VSgWUCiWUygvFjkql0hy7sHwxpoRCIfyzrcX2//xXvL0CCuGy+7u4XmFxf//8v1JheXyFaHu1WtWiXaSGDeglKm7MLyzCyVOn0SW6U4sdszVoc87i9BmtKDZsYC+ZsiFH1DM2Ct6e7igpuzQJxeI/1qJLVCdRp2Z7UFlVhQVLV4pirhoX9O9p3xMwEF2NVXFjLIsbiYiIiIiIiFrb4IF9sX/LSnzy9U949Z2PUFFRKVq/a+8B9BkxBY/NuRNz//UkPO3s3Jujy8jKFS0rFAqMGDEMx45fGnc4l5ePY8dPIrZrTGun16zCQ0NExY11Oj3Ss/PRJSJIxqyorYuNDBUVN6ZnncPPK7eZxxwvjFsK4mWFcOH/Vep6xiYvLgsWywqL+6hn2WJ/W7kGQqEQMKRbGFYkpplj+08VYvqgCLhr1DJmJl1hWQ1Ss0pEsaHxkbx2iJqFxtkJ/XtEYce+Y+ZYUvIB9O4Rh7iu9lcgdSItA9uTkkWxPt2i4ObqIlNGRC3vcIp4ApnuLG4kInJ4BQUFLXK/bm5uTS4gq6/DoaPy9PTEE088gfvuuw9xcY2/7s7T0xP3338/3nvvPQCAVqvFq6++irlz57ZUqs3i8u6MDzzwQKsdNyEhodWO1VJY3EhEdiko0B+/fv0h7rntJjz6/Bv/z95dh0d1pm0Av8fi7jZRkkASSAgECe5QSqGlQN1pu93K7ra731q73a7Xtq5sDSpAgQotUlyCBAmSICE2cXcd+/5ICZzMBEhGzky4f9fF1Z7n2DOTmcnkvOd5H5y7kC9YX1lVg7sffQYfrfoab734FwyLjxUpU7qcykhxY1hoMIbGROLLtet7YnX1DThx8jRGpoywZnpmFejvC4VcDrVG0xMrLa+CTqezmQELuj4NixuCH7buhF6vN1in1+uh0WihgdbIniLrla9EIkF0ZDiW3TzfIkWOsZFhCA7wRXlVbU9sd8Zhuy9u7N210dPDDSOGDREpGxqM5HIZlt44HR98cakosL29A198/R0evf8Ouxo4XvvNJoNO4bfOnw4nR8sVVhOJTafT4dQZ4YAeOzcSERERERERiUMul+PXjz2AJQvn4Td//ifWfvujYL1Op8Pr73+CNd/+iP/+/U9YevN8u7r+NlhpNFqUVRjeNDd9ykRUV1ejprauJ7Zr734MjY+167HD8LBgHDtxWhBTldeyuJFEFR8VAplMCq1WBwDQanXIL664yl4/k5jwfryGz2CJRHKFAkthgaRCLkNsRDDGpcRb5PN9XEIofjh4ARpd9zisRqfHgbNVmDUy1OznsqZ9ORW4fGTZ2VGB0cOurTMI0bWYO2Ekjpy6gI4udU/s86+/w5+f+SVcnJ1FzKx/Ojo6sXLNN4KYg0KOeVPSxEmIyEpOnhF2bkxOsO/JRoiIAHT/LcJLQn3+TbZ48WKLnO7xxx/HE088YZFjm8LdXZxJ0Ix1bQSAzZs3o7i4eECFfsuXL+8pbgSA1atXY+7cuTZdyHexc6OHh4fVuycqlUqUlJRY9ZzmNEiLG+3101nMvA0LHKzGlAtwRgoz+nlyE3Y1dYBBN/BdTXnYpr7MTDm3TnP1ba5EqzYIzRifgqxtq/HKe5/h7699gI6OTsH6nfsOImXKAvzm0Xvx+ycfHFjHIFNfZ1LZgHfVS037mJaYsr9k4Hn39ZwZK250cXZCXGwMQkOCUVp2af3ufRkYMTwRMpkJeYhIKpXCyckR6pZLr3uNVovK2gYEB/ob7qA38TNFZ/j+uGYmf5aagIPsVufq6oKYyHBcKCiy7onN/DrT6/XIKyjCwSNZmJw+xqzHBroHFiePTcHq77f3xE6cPoPzFwoQNyTK7OezhuLSchzIPC6ITUxLttvPWTIX83+fTk6IxegRQ3Hk5KXub6dyzuHwsRMYOyrFxPNZx8nsszh87IQglhQfjbEj2bVRHGL+AXR9ySsoQltbuyCWnDQUol4zILI1pnyv5UcSERER2ZzrcGzSXq9Ji/lnmcnPmQnJizl+YQpN59W36YNe22UQC/X3wOr3/40HbluAJ/74L1woUAnWl1dU4baHnsKKz77Em/9+FvEDvYZt6vi3CWOiEpPObeJr1ORxf6HyqlpodYb3Anh7eWLqpAn4+pvve2JV1TXIzjmL4UkJZs3BmoyNv6uqmgBHD+M7yAde9CFpqxrwvgCg7zJhck9TXiZ6E1+jehPyNmXMHxDvOoiJv3ucHKSICg3ABdU1FjQKmPJ76xo20euh0emgMXb7jJHHXVBSCYVCjtFJ5p+s1N3FAalxQTh89tJ9GrtOlWNiQiCcHe3zlsKmti7sz6kUxMYlRsBRYe7HY+LvDlNe42L+zhST3oT77Exh5HPU29MNt8wahy9+2NsTa2hswrrvt+DupYusmJxpNvywFXX1DYLYwpnj4eftIfJ1cMt+DvfJ5LwHfgBjk5PbB/vLu76hEcWlwu8HI9i58ers9VqKmPi+JqKr6KsQ0FIaGhosfo6+ulFmZGRg2bJl8PDo41rRVcyZMwdbtmzpWX755Zexfv36K+whrouP09PTU7QcGhoarF5YaQ72O/0bEdHPHB0d8MenHkL27vW4cdZkg/VqtQb/efN/iE6bi3++9iGaW1pFyJK61GqUVxnOUuqgUEAikWDa5AmCeENjE46fOGWt9CxCITe8QF5kpMCTyNpumD0N/r4+YqdhFl1dhjd6mMuYlESDDm0r13yDjs6B35giFo1Gg5WrN0B32Q0VMpkUE0bbb4dcsm1LbpwOd1cXQWzNhh9RUjaQGwmsq6q6FqvWfCuIOTs54o5FczjzPQ16J7PPCpYD/H0RGOAnUjZEREREREREdLk50ybg5M51eP63j8Gx17VrANi25yCGT16IR57+C1QlZSJkSABQVGo4FiiRSCCTyZCYMBSBAcJJUHft3S+4dm9vFHKFQay43HBMmMjapo9NgsyOu6JerrKmwWLHnjRc2NGwsa0LGw4UWux8lqTX67F6bz7aexUyT0q2z4lrybaljxyKYdFhgtiBw8eQdfqMSBn1T/aZ89h7IFMQi40IweS04SJlRGQdp3KEXRsdHBSIHxIpTjJERHRdG2ih37Vobm42iHl5eVnsfNdi7ty5A953wgRhfUF2djYyMjJMTclikpKS8NBDD+Ghhx6y+rmXL1+OBx98UPSf90DZ5zRLRERGRIWH4bvP3sS3m3fiqT//B6peA0f1DU3487/ewH/f/wzPPHYffvnA7XDrddM9WU5peRV0OsOZXS7OxBoTHQVlWAiKLxts3bPvAFJGJEFupEjQHkhlhoMlqtJyjBvFYiISl4+3Fx576G60tbVDo9FAq9NBq9VCp9NBq+3+f61O9/Oytiem0+l6tu1e1vf8/1W312ih0192/J+364712kerg1b38/qf9+9LaHCQxZ4nZydH3DhzIr7+YUdPrLauHt/+uA3Lbp5vsfNawubtewyKymZNGgsvzwF0NCa6Bm4uzrht4Ux8+MV3PbH2jg689eFn+M0vH0SAn6+I2fWtvqERb7z/CVpahZNh3Dp/Ot8vdF3oXdw4ImGoSJkQERERERERkTFOTo547ulHcefi+XjiD//E5p37Bes1Gg0+XLkWn3y1AQ/eeSv++KuHERZiuevoZEhVWmkQk8vlPROnTZ00AavXfdOzrrauHidPZSMl2T6LCeRyw45WNfVNaGvvgIuzkwgZEXUL9vfGk3fNQ15xJbrUmu5xzZ5xSN1l45R64bIePdvoDLbXQ9dr+fL1Wp3eIt23fDwt120hMsgTQ5W+OFtc2xPLOFuFlBhfJCi9LXZeSzh6oQYnCuoEsVHxYQj04fgOmZ9EIsEdN07GP95bi44udU/841Vr8fjyexAbEylecleRX6jCipVrBDEHhRx3LZwOqZQTvdLgdiL7nGA5IS4GCoXhZB1ERETUf1fqVJienj7g4yqVSoPYV199ZdIxLcnd3R3PPPOMKOdeunSpKOc1F/usFiEiuoKFc6dh5uSx+Pt/P8Qr730GjUYjWF9b14A//P01vPruZ/jt4/fjsfuWwcXFWaRsrx+19cbbTWu03T8fiUSC6VMm4dPPV/esa2puxtHjJzA2bZRVcjQ3tVpjECsutf2OVXR9kEgkcLVmgbcJA3l6vR46nQ6FqhKsXL2hJy6RSBChDDVHdn2aOi4Vx0+fQ15RaU9s9/5DGDk8AXFD7GOWz+LScmzevkcQCwn0w7zp40XKiK4XKQmxSEsehswTl2YobWpuwevvfYInH7kXgf621Q2urr4Bb7z/KeoahN9ZkobGYFxqkkhZEVnXiV4zCicnsbiRiIiIiIiIyBbFRCrxwxfvYMOP2/GrZ/+DkjJhQZ1arcF7n3yFj75Yh4fvWYrfP7kcIUEBImV7famoqjWIXT6J49D4WAQHBaK84tLPbPe+DAxPSoBMZlgoaOv6mqBSVV6NodGGN58RWZOHmwtGDuvneJ4p3R4lMuh0OkEh5aXJYC8WTOp7LV9WQKnTo6G5FdsyTggOGxUWOPCcrpayRILbZyTgn59noFN96f38xa48/GlpCpwd7ePWwqa2LqzZVyCIuTk7YMl0TjxNluPj6YZbZo3DFz/s7YmpNRq8+9HneOLhexAVYXu/B1UlZXj7f6vQ2dUliC+cOR5+Pn3fjE40WJzMERY3jkiIEykTIiKiwaevbpSmdqk0Vtx44MABk45Jtsk+rkAQEfWTq4sL/vWnp3DPkgV47sW3se6HbQbbVNfW4Xd/fQWvvPMJ/u+JB/HIPUvgzNkjLUatVl81HhUZgchwJQpVxT2xvfsPIjVlhF3OktS7sBYAWlrbRMiEyL5JJBLIZDKDzoOBAX4WL06XSiW4e/E8/OPNTwQFyytXb8DvnnwY7u6WmynVHNrbO/Dpl+ug0+l6YlKpBPfcegMUdtoVl+zLHQtnoa6hSVAgXN/QiFff/h+eePhem5k1vrKqBm988CnqexU2KkMCcf/SG3tmVSca7Aw6NyayuJGIiIiIiIjIVkkkEtwyfyZmT03H3159H2/+7wt0dHQKtunqUuOtFZ9jxaqv8ci9y/B/jz+IoEB/kTK+PvQuFgDwc/GSFjKZDBKJBNMmT8QXa9b1rK9vaETWydMYNTLZmqmahdrIeCgA1DU0WzkTItsglUohlQJyDKBYWSJB1hlhgZ6LkyMC/bzMk1wffD2csWhiHFbvvDT5XUNrF9ZlFOLOqTE2P0ai1+vx1Z58tHUKP4+WzUiBuwvvASLLSh85FHnFFTh0Mrcn1tHZiTc++BSP3n8H4odEi5id0IX8Qrzz0ecG3xdHJQ7B5DT77CBN1F+ncs4LloezuJGIBgmJRAIb/9puFX397bJu3Tr4+5v/epirq+uA971Sl0NLcHcXr6O9seLE/jD2XDU1NSE7OxuJiYkmHZtsiwnTTRER2b5hcdFYu+IVHNu+FgvnTjO6TWV1LX7z3IsYMvYGvLnC8CIOmUdfA1u1dQ2C5WlTJgqWW1pbcSFPeAHfHrS3d6C1rd0g3tfzQAOj1+tR19CE8/kqnD6Xh+Onz+PoybM4kZOLnNwCFBSXoaPTcBCZ7FOhqkSwHKkMs8p5A3y9sXD2ZEGstr4Bb374GdraDd/ntqKrqwvvfvQ5yiqqBPE5U8YhPNQ2Cspo8HNwUOAXd98MZbBwVvjmlla88tYK7N5/SFB8a216vR4HDh/Di298YFDYGOjvg8fvWwJnJ0eRsiOyrqamZhQUFQtiLG4kIiIiIiIisn1uri74z7O/Rt6hH/HE8rvg6OhgsE1HRydef/8zxIyZg2f+8iKqqg27C5J5GJv8FBBOgBo7JBphocGC9QcOZVo0L0vpa2LXLjXHRM1Np9OjU61FS7saDS2daG7rQkeXBlqdXuzUyIwKS4XjeuEh/lYpLpyYFIZ4pY8gdvBcFbYeL+1jD9ug1+ux4WARThbWCeIj40KRGm+dsWS6vkkkEty5YApGxEUI4p2dXXh7xSrsO3gEer24n9N6vR4HMo/jzQ9XGtwTlzAkHPfcPBNSKashaPDTarU4dUZY3JicEC9SNkREZE3+/v4IDAw0+z83N/M3xmhqajL7MQHAy8vLIsftzViXxrAw0/4266vz4+nTp006LtketmshoutCStJQbPj0DRw9kY2/vvQuNv6022Cb8spqPPWnf+PFtz7CH55ajgfvuMXo4B8NTF+FC6qSMsFyY6PhFzNbnwnQmOLScqNxNQfyTFLf2IyiknKoSiuhKquAqqQCLUaKSC8nkQCBfr4IDw1EeGhQ97+QQDg48P1tTzQajcH7KirCtBld+mPquFRkZZ/HhcJLBZYlZRV4e8Uq/PKhu+DibNkOkv3V1dWFDz79ChcKigTxkCB/zJuWLlJWdL1ydnLE4/ctxtufroeqrLIn3tnVhdUbfsCxk9m4e+nN8PP1tmpeDY1N+Hztt8g+m2uwLjjAF0/cvxTubi5WzYlITKd7DebJ5XIMi4sRKRsiIiIiIiIi6q/gQH+8/o8/4re/fAD/ev1DrFi11mBcqr29A6+++wne+3Q1Hn/wTjzz2P1Wvy432Gm0xsdEi0vL4enRPUu+RCJBgL8/Si4b92hobIROp4NUal9zlBeX9DEmyglfTdKl1qKktgXFVc1QVTehuKoFFfWt6KuO0c/DCeH+7lAGuCHc3w1KPze4OCmsmzSZTK/Xo7CkUhCLCgvoY2vzkkgkuGNGIv75eQY61dqe+PeHVXBykGFKUvAV9hbP5mMl2HFCeM+Jm7MDls2wv064ZL9kUikeWDwD76/ZijN5l+4n0Gg0+OLr73D8ZDbuXLIQPt5eVs+tobEJX3z9ncEYEADERYZi+dK5kMsH0GmWyA7lFarQ3t4hiI1g50YiIhJJXwV75tDQ0GDV812NpQorLVUIas9uueUWnD17Flu3bjW5qFQMLG4kouvKqOREfLfqLRw+dgrPv/Q2Nu/Yb7BNaXkVHv/9P/CfN/+HP/3qYdy3bCEcHHjh31QKufFfOWXlldBoNJDL5ejs7MRPO4SFpz7eXhgSE2WNFM2qd9HmRfZYqCm2LrUax06dw56Dx1HYxwDplej1QEV1LSqqa3E4KwdA9+txdPIwTB47EhFhtjkQQ0LFpeWC2Y4lEgkilKFWO79UKsEDyxbglQ++QG39pe5uBUXFeO3dj/HE8nvg7m7+mXgGor29A+98tAp5BSpB3MPNFQ/fuYiDEyQKN1cXPPnAEry/6hvkFgq7sObmFeIfr7yNRfNnYdL4NIvfvKPX63Ew8zi+/m4z2js6DNZHhAXhl/ctgZuLbRUtE1naidNnBMtDY6Ph6MjOpURERERERET2JiwkCG//51n83xMP4p+vfYCPvlhv0E2wra0dL765Au989AWeWH4Xnv7FffDx8enjiNQfcpnx65uq4lIkDeu+ebqysgrHT5wSrB8WH2d3hY0AoCox3tVNx26C/dbeqcGhcxU4dLYcpTWt0PWj01dNUwdqmjpwLK+6J+bv6YxRsQGYkBgCbzcnS6RMZtbQ1IrGFmE31MhQ6xQ3AoCvhzNum56AT7cIP5/W7iuAWqPDzBTrjc1ejV6vx/eHVQadJaUSCe6eOxruLnzNk3Up5HI8snQOPtmwA1lnCwTrzpzPw99ffhuLb5qL9DGpVrlvSK/X49CRLKz9dpPR8dAR8VF44NbZUCh4+zBdP05mC4t8A/19EeDvK1I2RERE3QWH1irQ8/T0tMp5lEolsrOzrXIuY0Wc17vm5mbo9Xqrdeo0N/u7MkpEZAZjUofjxy/fw/6NKzFrynij2xSXVuDR376A+PQF+N8X66FWq62c5eCiUBgvENVotSirqAIA7N1/EM0tLYL1c2ZOh7yPwkhbpio1XtyoYFHRNauurcf6TTvxp3+/i8++/nFAhY19UWs0OHD0FP7zzmf4zzuf4cDRU+jie9ymFaqExVDBgQFwcrJuwYWXhxueemApPN1dBfGSsgq8+OaHuJBfaNV8jFGVlOHlt1YYFDY6Ozni8fuXIIAzcJOInJ0c8di9t2Bi2giDdRe7OP7tpbewc+9BtLVfuSvvQHR0dGJPRib+8eo7WLnmG6MDeWNSEvDUg8tY2EjXpZPZZwXLIxKHipQJEREREREREZlDeFgI3nv5eZw/uAkP3LkYMpnhGFVLaxv+9doHiBo1C8/9+w3UNzQaORL1R1/jmqqfuzTq9Xps2rod+ssK1xQKOWZMnWyV/Myps7MLFVU1Rtf1NfEtGSqpbsYXO8/iT5/sx9d7c1Fc3dKvwsa+VDe2Y/ORIvzls4P4cNMpnC2uM8txyXIKenVtdHV2hL+PdW5AvSgtPhi3To43iH9zsAgbDhT22Z3Wmro0Wny5J9+gsBEA7p47CknRQSJkRdR9P9ADi2dg8ugEg3UdnZ34fO23eP29T5B95jx0Osu8l3Q6HXLOXcCbH36Gz1ZvMDoemp6agIeWzmVhI113TvQaC01ONPx9R0REZE1KpdIg1tho+rW55ubmazqXJSQlJRnELFWEaOxxkn3jXyhEdF0bn5aCLWs+wL5Dx/D8i+9gx75DBtsUlZRh+W+ex79eX4Fnf/MI7lw83y6L7cTmeoWZ8VQlZXBzccaBw0cE8SHRUYiLjbF0ahahKjZe3MjXztXV1jdi7cbtOHnmwjXv4+joABdnZyjkckilUmi0GqjVGjS3tF71onBRSTlWlpRj/Y87MHvKOEyfkAZZH7PqkngKiooFy5Hh4rRM9/PxwpMPLMUbH61BY3NrT7y2rh7/ffdjTJ04FgvnzYSDg4NV89JoNNi0bTe27Nhr8Jq/WNgYFmy9mV2J+uKgUOD2hbMwMjEOn3+zBXUNwosMldU1WPvtj/j2x5+QljoCk9PHQBlqWofd8ooq7DmQiUNHstDR2Wl0Gw93V9yxaDZGDIs16VxE9ozFjURERERERESDU2R4KFb892/4w5PL8Y//vofP1nxncB25uaUVf3/lHbz54Ur8+hf34amH74Gnh7tIGds3RwfjE76qSrrHDnPOnEOhSjjmMXH8WHh5WbeAyRxKysoFRZqXUyg44evVZBfWYvPRQhRUWLZLg06vx4n8GpzIr4G/pzNmpCiRnhgCqRU6h1H/FJRWCZYjQgOs0uGtt6kpEejo0mLjQeF4/fYTZThb0oC7p8UizM+1j70tK7+iCat2XkBVo2HB1tLpyRiTEC5CVkSXyKRSLJs3EYlDwvHFD3vQ2Czsxno+rwDn8wrg5+uNSePSMH5MKtxcXUw+b2tbGw5mHseeA5morqkzuo27qzNuXzAVyUOjTT4fkT06lSPs3Dg8IU6kTIiIiLqFhYUZdDk0RydHYwWSiYmJJh/3Whg7j6WKEN3dee3ycs3NzSguLhblOoK5sMKCiAjAxLGp2LZuBXbtz8TzL72NPQeOGmyTX1SC+596Fv/8ucjx9pvnGZ3hlIwLDQ7sc925C/koVqmg1Wp7YlKpFHNnTbfLX7J19Q2oqqk1us7Lk1+m+qLX67Hv8Ams37QTnV19d1F0dnJCeFgwlGEhiAgLRXhYCPx8vY2+VrrUapSWVUBVUtbzr7yy2mjBY2t7BzZs3oXjp8/h7sU3IDjQz6yPjwZOrVajtFw4U2lUhDjFjQAQHOCH3yy/HW98vBa19Zf+ENTr9di59yBO55zH3csWYUh0pFXyUZWU4bPVG1DW6zkCAA83VxY2kk0aOiQCf3riPmzYvBv7Mk8arO9Sq7H/0FHsP3QUvj7eCA8LQXhY8M//DYGri/FBvvb2DqhKy6AqLuv+b0lZnwN4F41JScCSG2fAld0a6Tqm0+lwMqf3bKUsbiQiIiIiIiIaTGKiwvHRG//EH371MP7+ynv4fN1Gg/GSxqZmPP+fN/H6+5/hN7+4H08+fBfc3dxEytg+Bfn74kJhiUG8obEJldU12Lp9lyDu5emB9HFjrJSdeRUUGT7Oi1ycHK2YiX1p7VDj6725yDxvOK5zOYlEgiA/L4QH+0EZ7I/wYD94ubtCLpdBIZNBq9NBrdGipa0DJRU1KCqvRnF5DUor66C5bNz9ctWN7fhq93lknq/EXdOHwt/L9IIaMg+9Xo+iXsWNUWF932NhaXPHREMhl2LDPmEhSGltG15cfxJzRoZiTmoY5FaaNLhLrcXGTBV2nixH75JqiQS4Y1Yq0odHWiUXomuRFBuOPz+6BOu2HsTBE+cM1tfU1mPDD1vx/ZYdSEkahujIcEQoQxAaEgQHhfGJEi536V6YcuQXqZB1MgdqjabP7dOGx2HJvElXnJieaLA7mSN8L45gcSMRDSqS7i/GZFeGDx+OLVu2CGLm6NzYu0DSWl0bASA9Pd0gZo7HZIyXl5dFjmuvXnrppZ7/d7PT67ksbiQiuszUCWnYmf4xdu47jL+8+Db2Hz5usE1ufhHuefyP+MdrH+C5px/F0pvmsMjxGnh5uMPT3Q2NzS0G63btPYCwQF84Ol7qdDZ2dCr8/HytmaLZ7D14pM914SZ2nxqsausb8fmGzTh7oajPbYbGxWBy+hgMHxZ3ze85B4UCURFKREVc+nLe2NSMjMPHsO/gEdQ3GH5pLiwpx7/e+gQ3zpyIGRPHsIujDVCVlBkUP4eHhYiYEeDv642nH74DH375LQpUwk6t1bV1+O+7HyM1ORFT0scgJirCIoXahapS7Mk4jMPHThgt2A0LDsBDdyxEgK+32c9NZA7OTo64Y9FsjEyKw9c/7ER5lfGJAWrr6lFbV4/jJy/NVOXh7gZHBwfI5XJIJIBarUGXWo3Gpmuf6cnf1wuLb5jGbo1EAApVJWhpaRXE2LmRiIiIiIiIaHCKjY7Ep2//G3/89SP428vv4MsNPxp036tvaMSz/3oN/33vYzzzywfx+IN3ws1NnC5d9iY8NAjIPGF03ccrv0JHu7CD0+wZ06C4hiIGW6PX63Eg81if68OCOImoMacKavDlrnNoausyul4qkSB5aCQmpA5DdFhgn51AL+fj6YbwYD+kj+y+nqfV6lBSWYMDWedx+FSu0Ull88ob8c/VmbhpXDSmjAhjF0cbUNvQjObWdkEsMlTcyUtnpEbC2VGOr3aegU536feETqfHpqMlOFlYh5vGRmCY0stiryGtTo/TRXX45mARqo10a5TLpLj3hjSkxoVa5PxEpnBxcsTdC6dhZEI0vty4Bw3NrQbbaDQaHMk6hSNZpwB03wsRFOiP8NAQeLi7Qi6XQy6XQ6vVQq1Wo7ml9YqTevfm4eaC226cwm6NdN1rbGpGoapUEEtOiBcpGyIiom7GuhwWFxebfNzex0hISDD5mNfKWCGlqd0o+9rfmkWbpjpw4AAyMjKQk5ODhoYGlJT0PWFYf5mj26etsJHiRsnP/0g8Yj7/vefU6gdTL47pTTi3qc+ZxJRiGRPy1l/9wobFzi3mz0vbdxe43iQApqenYtqGFfhp90E8//J7OHjslMF25y4U4s5f/B7/+O/7eO7Xy3HrjTMglRr5uWpNeNwyh6tvcwV6xcCLLi1RiKMMDULj2QuCmE6nQ2FhIfSaLgyJjgAAuLq4YMokw9kb7IFao0HGIcPOnxeFhwX38V4w8fmWmvArXdf37G3WsP/ISXy9cbvRgTVnZyeMTxuJSePTEOhvnkFQTw93zJs5BbOnTcTpM+exJ+MwzpzPE2yj0WrxzZbdyMo+j/uWzkeAn49Zzi06k37viaegSPjHVkhQIBwd+zPjr2Uet5eHK55efht2ZBzF9z/tF8yEqNfrcTTrNI5mnUZIUAAmp4/BmNRkOJk4U3FXVxeOZp3G7ozDUJWUGd1GKpVi7tSxmDtlPORy2cB+7vY6iG3P3w0t9Dq9+mlNPK8ZXivDhkTiz0/eh/MFxdhzKAsncnIFg+TGNBmZLOFaSCQSDI+PweRxKRgaEwmpVGL9z0aTnjNTcxXrvS3m7x/7/N1nbSezhV0bfX28ERzErr80WJl6PceU/S3wtyARERHZGSNjoiZfzzBtd/HY6/cgkZ5we71eB5j2lIl5HcWUa0am5G3qtSqt8YIpY+KjQrHq7X/gj08+gBdefR9rvttqsE1dfSP++PdX8eo7H+O3v7wXj923DK4uzsYPaMJYld6EMVGJzMTCQJPG7Q2Fhxmf6LSzsxM/bt2OqRPG9owpR0WEY9hQ++wYk5tfiPLKaqPrnBwdEBAUConU8L2g1xnvKHhN9CaOaZpybnX71bfpi0yKtg411u4+i8xz5UY38XRzwYTUYZgwcii8PEwrJJbJpIgICUBESAAWzhiDwydzsedIDipq6gXbqTU6rNt3AVl5NbhrRoLxLo4SU+8vGSC9mNdvTGDCZ3hhibCTp7urM3y93PtxBFM/y4z/rNMTw6D098DKn06jrFY4NlNa24Z3fzwDPw9HTEwIwrihAXBzMk+xdlNbFzLOVGH/mQrUtxj/3RYR6IV75o1GkK/HwE4iMWESc1NfY6b87hFrX1OZ8hls55JiI/DcL29D5qlc7DmSjdJK4xO+At33UJWVV6Ks/Mrdfa8m2N8Hk8ckYcyIODg5OqD/341NuTdx4Lt2M+F3jymvcXsdhhWTmPcg9fPcp7KFXRvlcjmGxkbBji+q9J89X1ewR/b6fJvyPdzYY7bX54HISox1OVSpVCYd01ih2/Dhw006Zn8lJiYiO/tSEwNzFGwaY+z5szVbt27Fyy+/LHgOek8yR5fYSHEjEZHtkUgkmD11PGZNTsPmnRl4/pUPkJmVY7BdzvkC3PaLPyLp9Rg8//TDWDR3qvEiR0J4aDBO9ypurKqqQkdHB1QlpYiJCodEIsHMaZPh5OQkUpamyTqVg+YWw9nWLuprQPN6pNfr8d3Wvdiy+6DR9eljUrF4wVw4O1vmtSCTyZCcNAzJScOQX6jCZ6s3oKpaeBG5sKQcL7/3OR6/f0n3TLskit6zp0VFhImUiSGpVIqZE9MwPD4Gn63bhIJiw0HpsooqfLV+Izb8sBVDY2MQoQxBeFj3PzfXKw9Qt7W1Q1VaBlVJGVTFZTibm4+29r4H0EOD/HHP4nlQhgTg+rx6TvZKIpEgPjoc8dHhaGhqwf7Mk9iXedJox+eBcHN1wYTRIzAxbQR8vT3NckyiwaR3cWNy0lCLTHZCRERERERERLYnIT4GX73/Iv70q+V44ZX3se6HbQbb1NTV4//+9hpeefcz/N/j9+PRe5ZYbPzG3gUH+kMuk0GjFRZxFBcXo729A5XVtQgO9O+eqHDWdLu9BrMnI7PPdWHBAd0TyxEAoK6pHW9+cxTVDW0G65wdHXDzrHEYNyIOMpn57zFwdnTAlLRETB6dgDP5Jfjqx32obWgWbJNX3oAX1xzGowtSEBPsZfYc6NoUlFYJliNCA2zm80EZ4IHf3TYOmzPzsTWzALpeN2LWNHXim4NF2JipwqgYPwyP9EG4vyu83Ryv+THo9XrUNndCVd2CEwV1yMqvhbaPiTDlMinmj4/D9NRoyBX8XUT2wdFBgYmjEjAhdRgKSiqx50g2jufkQaM1XxG5VCrFyIRoTB6diJiIEJv5DCGyBSdzzguWh8VGweEaOmQTERFZmrkLAY3tP3fuXJOO2V/Lli3Dc889J4g1NTXBw2NgE9M0NjYaxJRK5YCPZy2vvPIKVqxYAaD7b96L388t+T3d3gsnWdxIRHQVEokE86ZPwNxp6fhh2z785eX3cfz0OYPtTp/Nw63L/w/JCbF4/plHcNPsybxQ1Evv4jC1Wo2ysu7OY+0dnaiqrkVqynCkJFt3lghz2pNxuM91zs5O8PPxtmI2tkun02PN99uw59Bxg3Venh64c8lCJA6NtVo+0ZHh+ONvHsP3m7djx54Dgi94LW3teG3FV/jFPYsRG2U/bcwHi66uLpRVCGcljAy3neLGiwL9ffD0w7dj+/6j+GH7fnSpDWcP7uzswonTZ3Di9JmemI+XJ4KDAuDg4ACFovuruUatQZdajYqqatTU1hscxxi5TIbZk8dg7tRx3d0aieyYl4cb5s9Ix7xp41BWWQNVaSVUZd3/SsurBV1SjZHLZAgJ8kd4SCDCQwMRHhKE0CB/i9wYQjRYXP67CQBGJAwVKRMiIiIiIiIiEsvwYbFYu+JlZJ0+ixdeeR/fbN5psE1VTR2efv4VvPTOp/j9Ew/g4bsWw8nJUYRsbZdCLkdIkD9UpRU9saamJtTXd1/vLyouRXCgP9JSUxAYGCBWmiZpbGpG1inDSYEv4oShl1TUt+Ktb4+hoaXTYF3CECXumD8J3h5uFs9DIpEgIUaJPz5yK77dfgh7jgh/fu1dGrz17TEsn5eMhAhfi+dDQnq9HkW9ihujQm3r80Euk+LGcUOQHB1gtIsjAGi0ehw6X41D57u7uro5yaH0d4PSzxVB3s5QyGVwkEmhhx5qrR5dai3K69tQXN2K4ppWtHVevTtrRKAX7p6TjCCf/nS1JLIdEokE0cogRCuDsHh2Oo7l5KGwtAqq8mpU1jT062ZkCYAAPy+EB/sjIjQAqYlD4OnmcvFElnkARHbqZI7wXs/kxHiRMiEiIhLqXQiYk9P39ZZrcfr0acGyUqmEUmnd+56NFTdmZGQMuMjy8uLPi+bMmTOgY1nLli1b8OGHHwrqSOy98NAaWNxIRHSNJBIJbpw1CfNnTsR3W/fg+Zffx4mcXIPtTuTk4uYHnkHq8KF4/umHMX/mJBY5/iw6IgxSqRQ6XffMYyUlJdBeNmtpbn4hfv/0E3b7fJ09n4e8gr5bgoeHBtntYzMnvV6PtRuNFzZaulvjlTgoFFi8YC5ShidgZa8ujh2dXXjn06/x5APLEBUeYvXcrmdqtabnM+Mid7crdzsUi1QqxaxJaUgflYSDx7Kx53AWqmsbrrhPXUMj6hoMZ5a5Vt6e7pg0Jhnpo4fDw0afF6KBkkqlCAsOQFhwANLRPfGBVqtFeXUdmppb0KXWQqPRQK8HFAo5HBRyuLu6IDjAj0W+RP3Uu3PjiEQWNxIRERERERFdr1KShmL9x//F0RM5+Osr72HjT3sMtqmoqsGvnn0RL73zCf7w5IN48Pab4ejM208uilSG9BQ36vV6qFSXxg9r6+rRpVZj6uQJYqVnsl37DhqM3VwugsWNAIDK+la8vv4omtu7BHFnRwcsnj0e45LjrD527OSgwLJ5EzFyWDRWfb9b0MVRrdHh/R+y8Mj8ZCRE+Fk1r+udWq1Ba3unoBgp2N82J02+2MXx4Jky7D2pQmmNYZHjRS0dGpwpbsCZ4gaTzxsR6IXJyZEYPTQEMikns6TBwd3VGVPSkjAlrXu5o0uNkooaFJdXo7y6Hl1qDdQaDTQaHeRyKeRyORwVcgT5eyM82B9hQX5wcnQQ90EQ2YmT2cLixuEJcSJlQkREJNS7ELC4uNikLof79+83OL4Y5syZgy1btvQsnzp1asDFjadOnTKIPfLIIwPOzRqeffZZSCQS6PV6eHh4YO7cuZgwYQISEhLg5eUFd3fzTthTXFyM5557DgcOHDDrca2NV5eJiPpJIpFg4ZwpWDBrEjZs2oW/vvoBTp/NM9ju2KmzuOm+3yAtJQHPP/0I5k5Lv+4L29xcXZCSGI9jp86gpaUFNTU1gvVyhQPOXShERLj9dcfr6OjEqrXfXnGbxPghVsrGtm3ctg+7DwoLGyUSCe649SZMGDtKpKwuiYkMx+9/9Sje/+RLnMvN74l3dqnx1idr8evltyMs2LZmyhzMXFyc4e7mhuaWSwNjhapSBPjb7qCqq4szZkwcjWnpo3A2rwh7DmXh1Nk8s868khAbicljU5AYF81udHRdkclkCAvyB4L80T0fKRGZqqWlFXkFRYLYCM5WSkRERERERHTdG5WcgO8+ewOHj5/CX19+D5t27DfYprS8Co//4V/4z5sf44+/fhj333YzHBwUImRrW9JSErHn4DEAQFVVFdrb2wXrG1va4eBgn8UIJWUV+GmX4WvhctERoVbKxnbVNXfgrW+PGRQ2Bvt745e3z4O3p+W7NV5JXGQI/vjIrfhk/Xacyr1UfKvV6fHhppN4/KZUxIR4iZfgdUahkMPFyRFtnZdeL2XV9QgO8BExq77JZVJMTArDhMRQ5Jc3YO+pEhzPrYBWZ94uFAqZFKOHhmLiiAhEBHqZ9dhEtsjJQYEh4cEYEh7cHbjO7zEjMhedTodTZ84LYiNY3EhEg4xEKoGEtxDa7XOwbNkyrF69umfZlC6HvYvbBlLc2NTUhE2bNkGlUmHChAlIT0/v9zEeeeQRQXGjKUV3lx8H6H5MAy3+tIYtW7agqakJEokEL7zwApYuXWrxcyqVSrz++usYM2YMAKClpQVubuJeexoIO30LExGJTyqVYvH86cj66Qt89e4/MSw2yuh2mVk5mH/3U5hw0wP4ac/B676t8ORxowxmKAV+7s4UFoaNW3agvKJKpOwGbsMPW1FX39DnerlchnGjk62XkI06dDwbm3YKv6RKpVI8eNcSmyhsvMjJ0RGPPXAnkpOGCeLtHZ1459Ov0drW3seeZG4SiQQxUeGCWF5hUR9b2xapVIKE2Eg8etci/P23D+PuxXMxZdxIRCmDoZBf+xwjcpkM4aGBmDQmGXcumo0Xnn4Ij993K0YMG8LCRiIiMtnpM+cFf6NIpVIkDuWAHhERERERERF1GzNyOH74/G3s//5TzJ4y3ug2xWUV+MVvX0B8+nysWPU11Gq1lbO0LdHhoQgLDoBarUZpaalgnYuLC3R6Cbbu3CtSdgOn1Wrx2Vfrr9i1MSE2Cr7enlbMyvZ0dmnw9nfHUN/SKYhHhPjjV/csEL2w8SInBwWWL5mNtCThBL1qjQ7vbcxCVUObSJldfyQSCaLCAgWx/OIKkbK5dhKJBDEh3rhvTjL+/sBULEyPQ7zSFy6OAy9yd3N2wLAIfyyenIC/L5+JO2cls7CRiIhMUlBUYnCfF4sbiYjIlixfvlywfHmhY39s3rwZTU1NPcsPPfRQv4sAi4uLMWPGDDz33HNYsWIF7r//fjz55JP9ziUxMVFQWJmdnY3s7Ox+HycjIwPFxcWC2DPPPNPv41jTjz/+CIlEgtdff90qhY0XeXh42HTR57Vg50YiIhNJpVIsvWkWFs+fjjXfb8MLr36Ic3mGhTcHj53CnNsfx8QxKfjrM49i2oTRImQrvtjocECvRWtrqyAeEhICBwcHaLRafLZ6A555/CHIZDKRsuyfs7l52HsgUxCTy2TQaLU9y6OTE+Hm6mLt1GxKQ2Mz1ny/TRCTSCS4/45bkZqcJFJWfVMoFHjwriV4/5MvkX02tyfe0NSCtT/swH1L5ouY3fUlOlKJrFM5PcsFRcXQarV28xkBAN6e7hifmoTxqd2vda1Wh4rqWqjKKtHY3AqNWgO1RgM9AAe5HHKFHO6uLggPCUBwgB/kcvt5rEREZF9OZp8RLMcPiYaTk6NI2RARERERERGRrRo/Ohmbv3oX+w4dx/Mvv4sd+w4bbFNUXIaHn34e/3p9BZ59+lHcdeuNkPdjsr/BQiKRYPK4VOw7cBjay8YLASA8PBwSiQQ//rQbwxOGIiwkSKQs+2/L9j0oKbtywdXkcSnWScaGfXPgAirrhYWB4cH+eOKu+XB2tK2OnTKZFPcsmgZIgMxTF3ri7V0arNqWjV/dkgqplJ3DrCEmPBDZeZdu2CworoROp4NUah8Tnbq7OGLW6GjMGh0NvV6P2qZ2qKqaUFzViOKqJjS3d0Gt0UKt0QESwEEug1wmhaerE5SBnggP8IQywAve7k6QsFsdERGZ0Ynsc4Jlf18fBAX4iZQNERGRIaVSKejeeLGgT6lU9us4H3zwQc//e3h44Le//W2/c3nqqacEBZJAdyfCjIyMfndwfOaZZ7Bp06ae4z377LNYv359v47x3HPPCZZff/11my/gKy4uRnp6OmbPnm31c3t6eqK5udnq5zWX6+8qMhGRhchkMty+aA6WLpiJL7/ZghdeXYELhcUG2+07nIUZSx/FlPGpeP7pRzBlvO10q7OGrq4uQNMliDk5OSEw8NJMhEXFpdi6cy/mzZxq5ez6r62tHavWfCuIyeUyaDTCgcrJ46/PYtaL9Ho9vvhmC9o7hDOU3r54AUal2F5h40VyuRzL71mGNz/8DHkFl7qNHj6ejdSkeIwYNuQKe5O5REcIOzd2dalRUlaBCGWoSBmZTiaTIjTIH6FB/mKnQkRE17mT2WcFyyMS40XKhIiIiIiIiIjswcSxI7Ft7QfYnXEEf3npXew5eNRgmwJVCR546s/452sf4NmnH8XtN99w3RU5hgX7o76+XhDz8fGBu7s7gO4uiCtXb8DvnnzYLiZzLCmrwI/bdgtizs5OaG/v6Fn28fJAUnyMtVOzKedL6rD3VIkgFuTnjV/eMc/mChsvkkokuHvBVHR0dOFU7qXx0PyKRuw6WYzpKeFX2JvMJbpX58b2zi6UV9cjNNBXpIwGTiKRwM/TBX6eLkiNvUoBt/T6+t1ARETWdzJHWNw4IiGWhfRERGRznnnmGUGXwqeeeqpfhYCbN28WdEZ8/fXXB5RHX90V9+/f3+/iRg8PD7z++uu4//77e4793HPP4YUXXrim/V966SVB18aHHnoIc+fO7VcOYigpKcHtt98uyrnd3d2h1+vR0NAANzc3UXIwhX1M70REZEdkMhnuWnwDcnavwUevPofoCOPFN7sPHMO0Wx/BzKW/wP7MLOsmKaI9+w/Bx9sDMtmlX0FKpdJgxsHvN+/AoaNZVs6ufzo7u/D2/1ahrr5BEHdyFHa6CQ8LRmS4/RZhmcOh49k4fS5fEEtLHYGJ42y/6NPBwQEP3LkEzk5OgviX32xB22UDtmQ5bm6uCAoUFgHmF6r62JqIiIj6o3dxY3LSMJEyISIiIiIiIiJ7MiV9NHauX4Ftaz/AhLQUo9tcKFDh3sf/iKTJi/DFuh8MuhgOVnq9Hjt27UdYcEBPTCqVIiwsTLBdcWk5Pl/7HXQ6nbVT7JeGxia8/8mXgjwlEgm6OoUT2k4ck2w3XeYsobNLg8+35whiCrkMjyydDTcXpz72sg0ymRT33zIDft7C7gffH8g36EJJluHp7mrw/OeprtwplYiIiK7uVM55wfLwhDiRMiEiIuqbh4cHPv74456uhNnZ2XjppZeuad/s7Gw8++yzPcuvv/56vwsRL8/DmPDwgU18lJ6eLii0XL16tUE3RmOee+45rFixomf5oYceGlAnSjE0NTUZXAO0lvXr1yMzM1O085vq+r2qSERkYXK5HPctW4Azu9fhw5f/jIiwYKPb7difiUmLHsLcOx7HwaOnrJylddXW1ePgkWNQyOWIiehul+3p6QkvLy+j269c/Q2yTuUYXSe2LrUaH3z6JQqKhN05vTw90NIqHOCZkp5mzdRsTkNjM9Zu3C6Iebi7YcnCG0TKqP+8vTxx603CGT8am1uxptfjIsuJiRT+cXR5J00iIiIaGL1eb6Rz41CRsiEiIiIiIiIieyORSDB94hjs+fZjbFnzAcaNSja63fm8Qtz12P9hxNSbsebbzTZfzGeq7DPnoSopRaQyBFJpd1eY4OBgOPaaIBUADh45jnXfb4Zer7d2mtekpbUNb37wKWrrhF0oPdzdoL3s5yiTSZE+aoS107Mp3xy4gNpm4cSoC6ePQYCvp0gZ9Y+jgwJ3LZgiiKm1Ony+/Qx0Ott8fQ420Uph98b8kkqRMiEiIho8TmQLOzcmJ8aLlAkRkQVJJPx38Z8dUyqVWL9+PZTK7vvrV6xYcdVCwIyMDNx3331oamoC0F3YaEp3w2eeecYg5uHhgXnz5g34mHPnzjUocJw5cyY2b94s2K6pqaln3erVq3vO/cILL9hNYSPQnXNfdRHW4O7uLtq5TcXiRiIiC1Mo5Hjw9kU4t3c93v33H6AMCTS63dbdB5F+0/2Yf/eTyMwy3tbZ3m3etrNnRtaYSCU8PdyvOJuDTqfDipVrcOjoCWuleE3aOzrw9ocrceZ8niDu5uqChsYmQSwyPBRjU4dbMz2bs2HLbrR3dApity9eADdXF5EyGphxaSORODRWEDt8PBsXCor72IPMKSYqQrBcWl6BdnbOJCIiMomquBSNTc2C2IgEDugRERERERERUf9IJBLMmpKO/T+swo9fvoe0lCSj2505n4/bHn4GKdMWY93GnwZlkWNXVxe27tgDAHB1dkJcdAQcHR0RFBTU5z479x7E2m9/tLnno7GpGa+99zHKK6sFcU8Pd4NrSlPHj4KHu6s107Mp+eUN2HuqRBCLCQ/ClDHG3wu2KjYiGFN75Zxf0Yi9p0tFyuj6EqMUfk6UlNegq0stUjZERET2r7mlFfm9GheMYOdGIiKyYUqlEtu2bcNDDz0EoLsQMC0tDS+99BIyMjJQXFyM7OxsrF69Gvfffz/uv/9+NDU19RRGmlLYCADLli3DCy+8AKVSCQ8PD6Snp2P9+vV9dnS8VnPnzkVmZibmzJkDACguLsZTTz2F+Ph4pKWl9fz3ueeeQ3FxcU8u27dvx7Jly0w6t7WFhYX1PAbqH7nYCRARXS8cHBR45O7FuG/pAvzvy2/xzzc/QllFtcF2m3ZkYNOODNw4cxKe/90vkToiwSL5dKnVKK2oQXFZJYpKK1BSXoXG5lZoNFqo1RpAAijkcijkcni4u0EZGoTwn/+FBQdCoejfr5DzF/KRm1fQsyyVSvDAHbci43jOFQfqdDodPv1yHRoaGzFr6kRIpeLW5VfX1OF/q9ZAVVImiLu6OMNBoRDE5HIZ7ll6k+g5i6mxuQVHTwq7AaWljkBy0jCRMho4iUSCO269CX9/+W20d1wqqtt14BiGRClFzOz6EB4WArlcDo1GA6C701Tm8ZOYOG70df0eIyIiMsXpM+cFy16eHggLNd5xnoiIiIiIiIjoaiQSCeZOn4g50ybgh5924/mX3sGxkzkG250+m4slD/4ayYnx+Mszj2HhjXMhseDs+s0trVCVVkJVWoHSymp0dHShS62GRquFTCqDQiGHg0IBf1/vnvFQf1+fnq6L/bHvQCaami8V/kWHhyIwKARtnVcuUNq17xDa2ztxx5KboJCLfytPdU0d3vrwM1TX1gniPt5eBoWNAX7eWDBrojXTsznbjxcJlhVyGe5aMAVSO+wacdO0NJzOVaGm/tKkvjuyVJg0PNQuH489iQwNgEwq7emKqtXpcEFVgYQhHIsmIiIaiOyzFwTLMpkMw2KjRcqGiIjo2v32t7/FI488gk2bNmH16tVYsWIFVqxYYbBdeno6li1bZnJR4+WWLVtmkYJCDw8PvPHGGyguLsbmzZt7ijWLi4vh4eEBT09PKJVKzJ07F/PmzTO5oFIs6enp2L9/P2bPni12KnZH/CuiRBDx4qeY1131JuxrUt4iFoDoTZxp0qQL5TLTzm0KjbBjnaNcgsfuXoQHlszDh19+h3+99SkqqmsNdtu4bS82btuLhXMm4y+/eRgpiQOYNUgmLLarqm3AviOncfaCCuXVtdDprvxC1Gi0aEcnmlpaUVJeiQNHujsoSqUSBAf4ISEuGhPTkuHv6y3cUSrvdRwNtmzfJYi5ubpi8U1z4eXtg43b9lz1oXz74zaczD6Lu5fdjKAA/6tub246nQ67Mw7j2x9+QpdaOPjo7OSE6MhwnMo5J4gvmD0VQQF+1klQYsJrfAADswJXeG9nHDktKF5VKBRYsnDg7cnF5u3liRtmT8W67y61Q8/KyUVDUzO8POy3lbc9kMtliFCGIK9A1RPbsScDJ7PPYNrEcUgYGmvRGx+uO3pTvqjAxN/ZJjA1b9NOLuK5TSC5XouDRfsybtrrVMzPOXvN21Qmfa6I+LlwjXnnnM0VLF/sUq0X7fNUxPcmEREREREREZmNRCLBjbOnYv6sKfhu8048/9LbOJF9zmC7E9nncMv9TyF1RCL+8rsncOOcaWa51t/Y1ILDWdnIKyqFqqwSDY3NV9+pFydHB4SFBCIiLBipSUMRqQy5am71DQ3IOHxUEIuJisDM6VPw77c+hkajveL+h45mobi0DHcvuxkRytB+52wOOp0O+w4ewYaNW9HZ1SVY5+XpARcnR9TVN/TEJBLg7sXzDCaBvZ7Ut3TgVEGNIDZ/yigE+HiKlJFpHB0UuH3+JLy56oeeWG1TB86o6pAY4StiZoOfg4MCoUG+UJVdmiR73ZYM5BZFYkpaErw8rt/uqERERAORc15Y3BgbHQEnJ0eRsiEiIuofDw8PQaHhxUJAT09PeHh4QKm0z4lwlEolli9fjuXLl4udikXMmzcPt956K/7617+KnYrdYXEjEZFInJwc8cT9S/DQ7Qvw3qoN+M87K1FVU2+w3bdb9uDbLXtw+8LZePffv4eHu1u/zqPV6nD6fAH2Hj6FM3mqq+9wDXQ6PUorqlFaUY2f9hxCQlwUJo9NRVJ8tNEOaoeOHEdtnfCxzZo2GY6OjpgzLR1Z2edQUl551fMWFJXgn6++iwVzp2PG5HSrdWurrqnDyjUbcCG/yGCdu5srEofG4uCRLEE8MjwUMyaPs0p+tkqr1WHv4eOCWNrI4XBzte9Bl/FpI/H9pu09Ra46nQ4ZmSdxw4wJImc2+MXFRAmKGwGgprYea7/dhMADmZg2cRziY6NZ5EhERHSNzvQa0BsWN0SkTIiIiIiIrhMSiZEJYEy8lmWvl8JEnaTKFPb6hIvIpOu1Yk6CY6cTHpnyuE19X+o0BiEJgIVzJmPBrIn4ZtNOPP/yuzjdq3sKABw7mY2Fdz2K0SmJeOWF/8OkcaOu/bzS7glA9Xo9cgtLsOdQFrKycwWTbw5ER2cXLhQU40JBMbbvPQxlSBAmj0tFWnICHBx+LuTr9fresn0PNFptT1wqlWLe7Onw9/PFjbMm45tNO6963rKKKrz05oeYNW0ibpg11apdHGtq67FqzTc4n1dgsM7P1xt+vj44ez5PEJ8+YQxiIiOu+RwSEyZd1TuaViwoMWWy2M6mPldlHDsN3WXvHweFHBNTEwZ+LhsQHxmC0EBflFZemqR576lSJEb0YxJiI58JVmPSxMBiTgopxZDwEKjKLhXL6vRA1plCnDqnwsiEGEwanQAPNxfDXU0dH9Wb8LilJjzfpuZtyv6m/N6z14lixSTmY74en2+T/94y4XuUqZPrmpS6KZO1mnJe004tLhE/C63wN9eZ8/mC5WGx0XZ8PYLICq7H35lEdkSpVNptQeP1JDExEePGjcPzzz+P559/3qrnXrt2LVQqFR555BG4ufWv3sQWXK9tOoiIbIazkxN+/dDtyNu3Di/+6XH4+XgZ3e7Lb7fizy++d83H7exSY/PuTPzltU/wwZc/mK2w0Zic8wV4b+U6PPfy+9iy+6BgFs/m5hbs3ndQsH1YSDBGJA0DAMhkMjx4x81wdXG+pnNpNBps2LgVr7y9AtlnTR+YvJLm5hZs2rYL/3jlbaOFjX4+3kgdkWhQ2KiQy3HP0pusVnxpq06dzTWYCXdy+hiRsjEfF2dnpKWOEMT2Zp6AVnvl2XbJdKnJiYiJCje6rrKqBl+t34gPP/0KF/ILRew4RUREZD/O9LoZbVhcjEiZEBEREREREdFgJpVKccv8GcjavgarP3gRCXHRRrc7kpWN6Tffj+OnzlzzsTu7urD74HH8/Y1P8NqK1Th26pxFxg+Lyyrw+fof8Yd/vYWvN25DVU2dYH1efiHO5gqvtYwZlQx/v+5OdzMnjUVstPExjt50Oh22bN+Df//3PeQVWm6M9yKNRoPd+w/hH6+8bbSwMSQoAMrQEIPCxgA/HyyYPdni+dkyrVaH/Sd6/dyHx8LZyUGkjMxDIpFg8mhhgWZ2YQ1qm9pFyuj6kZoQA08jxYtanQ5HTufizZUbsXnvMbS08mdBRER0NWdzexU3xhv/O4SIiIjInF5//XX8+OOP+Omnn6x63i+//BIrVqxAQ0ODVc9rLuzcSERkI1xdnPHMI3fi0btuxtufrsNL761CXYNwBsgfd+zHG3975qrHOl9UjlUb96G2oeWK20kkEgT6+yE8LAThYcEICvSHg4MDHBTds412qdXo6upCRWU1VCXlKCopRVV1bZ9FQ3UNTfh2y27szzyBu269EXHREdi2ay+61F2C7ebNmiborBbo74snHrgdr324Ch2dXb0Pa1RBUQneXrES/r4+mDh+NManpcLN1cgMhf2k1+uRX1SMPRmHcfxEdvfsqkZMHDcaEokEuzMOC+ISiQT33b4IQQF+Judi7/YcPCZYjgwPQ3hYiEjZmNfk9DHYf+hoz3JjUwtOnrmAkUnxImY1+CkUCty1dBFy8wqxc98BlFdUG2xTVlGFVWu+hTI0GNMnj0dUBGeqISIiMkav1xt0bkyIjxUpGyIiIiIiIiK6HkilUixZMBu33DADa77bihdeeR/n8goF22i1Wnz+9UaMHD7sqsc7m1eMz7/bgbqG5qtu66BQQBkaDGVYCHy8PKFQyCGXy6HV6qDRqNHa1o7i0nIUl5ajsanv47V3dGDH/kzsOnAUN0yfgDnT0gEAm7fvEmzn6uKCqRPHCx77o/fcitc++BzFZZVXzRcAyiur8MpbKxChDMXk8WkYNXJ4zziuOdTVN2DfwSPYf+gomltajW6TPiYVra1tOH4yWxB3cnTAg7cvNGs+9uhEbimaWjsEsUmj7Ltr40Wjk4Zgw7aD6OhUA+juj7TvdCkWpg8RN7FBztXFCY/cNhf7j53B4ZPnodYI71fQaLU4dOIcjmVfwJgRcUgfOQwuzo4iZUtERGTbcs71mug1lhO9EtEgJZGa3oV4MOBzQDbCw8MDH3/8Me6//340NDRgyZIlVjlvc3P3NU0vLy+rnM/cWNxIRGRj3Fxd8H+P3Y3H7rkFL77/Bf7x+kc96/KLSlFT19Bnd8eOLjW+23kUu4/0PZupi7Mzxo8ZieTEoQgLDYaT49UvdF9+k3NHRydKyspx4vRZZGQeQ3t7h8H2NXUNeO2DVRg+NBqlJcWQy2Q960aOSEJoSLDBPuFhwfjFvcvwzidfobNLfdWcLqqurcOGjVuxcfMOjEpJQnxsDMLDQhDo73vNnRPbOzpQXFKOouJSZB4/iZKyij639fH2wqL5s3D46AmcPnPeYP1dSxZc02DrYNfR2YVzecJul4Oha+NFytBgREUoUVBU3BNjcaN1SCQSxA2JQmxMJM6ez8POfQdRVV1rsF1xaTk+/XI9oiLCMH3SeCgHSWEtERGRuZRXVBncqDcsnjcmEREREREREZHlyWQy3H7zPCy9aTa+3LAZz7/8HvIvG3PJPH7qivu3d3Rhw9b92H80u89tFAoFRqckITYmCuFhIQgK8LvmscPGpmaoSspQqCrBoaMnUFffYLCNTqfDxm17kZV9HkmxEaju1clxxpQJcHJyEsScnZzw+AO34dX3V6HSyNhGX4qKS7GyuBTrvt+C8WkjMXZ0CkKCAq758Vyuo7MTeflF2HvwCE7lnOtzUltvL08smj8LBw4fM+h4o5DL8Yt7l0AZEtTv8w82R84Ix0OjlYEIC/IVKRvzcnJQYOyIOOzOvPQ+O5JbweJGK3B2csTM9BSMS47HvmNncPT0BYNJmdUaLfYfO4Mjpy9gbHIcxo8cBidH++4YSkREZE7t7R0oLC4VxIb10UGeiIiIyNwSExOxbt06PPDAA9i8eTNef/11uLm5WfScjY2NFj2+pbG4kYjIRrm7ueIvv34Ir77/Odo7OnvimVk5mDc93WD7q3VrjFSGYlL6GIxKSTJpBk0nJ0cMiY7EkOhILJg7HUezTmN3xmGoSsoE2+n1eqxevxF6nRYjEuLg6+0JRwdHzJgysc9jx0aH48mH7sTbH3+FNiNFk1ei1mhw8EgWDh7JAgA4OjpAGRKM8LAQ+Pp4Q6GQQ6GQQ6fVQa3RdBdplldAVVyGqpprGzycOG40lKHB+HLd9wZFnRIJsGzRDRg/OrlfeQ9WJWWVgsFQqVSK1BGDY5bSi9JGjhAUN6pK+y6KJfOTSCQYFj8E8bHRyD6bi137DqK2rsFgu4KiEvyvaC1iYyIxbeI4hAQHWj9ZIiIiG9S7a6OLizOUoYaTkBARERERERERWYpMJsNdt86Ht483Ftz5WE/86MkcaDQayOWGt7SczSvGqm93oL7ReHfFAH9fTE4fg3GjU+Di7DygvDw93DE8IR7DE+Ixf/Y0ZJ/NxZ6Mw8g5d8GgGLBAVYLvf9yM6PAQxEQqIZVKERwYgJQRiUaP7e7mil8/fCfe+ugrlJRX9SuvtvZ2bN+Tge17MgSdKMPDQqAMDYaLsxMUCgVkMik0ag3UGg3qGhqhKimDqqQMxSVlqKiq6bOg8aIJY0chXBmCr9ZvNBgTlctlePiuWxAbFd6v3AeronJhUev4lME1Eer4lHhBcWN9cwea27rg7sIiOmtwc3XG3EmpSB85FHuP5OB4Th60Op1gm84uNfZkZiPz1AWMHzkUY0fEwcHh+u6oSkREBADnLhQYfO+Nj4kUJxkiIiK6LimVSvz000947rnnkJaWhjlz5mDZsmUYP3682c914MABNDU1QSKx3xamLG4kIrJhcrkco0YMxb7DJ3pih7OyDYobdxzKxrpth40eY0hUBG5ZMBeR4aFmz8/BwQHjx6Ri/JhUFKpKsO67zcgrVAEAampq0NraCgA4ePQkEuJi8Oj9d8DNzfWKx4wKD8VvHr0H73yyGnX1A59BoLOzCxcKinChoOjqG1+BXC7HqOQkjEpJwp6Mw9h38IjBNlKpFPcuW4i0kUkmnWswUZWWC5ZDggLg4DC4BrkilMJOgBXVdejs6oLjIHuctk4qlWJ4QjwSh8biZPZZ7Np3CA2NTQbb5eYVIjevENMmjcOUCWNFyJSIiMi2nDmfJ1geGhsDqVQKvV7Xxx5ERERERERERJYxZuRwwXJbWztyzuVhROKlQjG9Xo9vfzqAn/YfM3qM2OhIzJs1BfFDos16E8/FcYjhCfGoqa3Dzn0HsXv/Yeh+LjAqKSmBRqPB+XwVKmvqkJaSiHmzpl2xq6KHuxt+9fBdeOeTNcgvKhlQXl1qNfIKVT1js+YwJDoCk8ePweFjJ/Dl198brHd0cMCj996K+OgIs53TnjW1dqChpV0Qiw4dXJNshgb4wEEhR5da0xNTVTchMcJPxKyuPx5uLpg/dTTSRw7FniPZOHm2ALpexRrtHZ3YceAEDmadxU3TxyI+OkykbImIiGxDTq+x0MjwULi6ugA6bR97EBEREZnuzJkzBrHbb78dSUlJ+PDDD/HAAw8AABISEpCUlAQPDw94eXkN+HwNDQ1oamrCmjVremKW7hBpKSxuJCKycWkpiYLixsysnJ7/1+v12Lj7ODbvP2Gwn0KhwMIbZmLqhLFXHDwzl8jwMPz6sQewc99BrP9+C6qqumca1el06OjoQL6qFNX1zdDr9VcdUAwJCsCffvUwNvywDfsOH7/itg4OCqjVmqvOMNpffr7emDQuDTFR4Th64jQ+WrUWHZ2dBtt5uLninmULkRAfY9bz27veXQyVYSF9bGm/QkOCIJVKewav9Xo9SsqrEBPBgSIxSKVSpAxPwPCEeBw/mY09GZloajbsZLtr3yGkDE+Ap4e7CFkSERHZjt6dG4fF8fssEREREREREYnD388HUeFhKFBdKvQ7fPxUT3GjTqfDF9/txIHjhjcHOTo44OYbZ2PiuNEWHxP18/XBkoU3YExqMj77agPyCgpRU1PTk2NdfSPyVRVwc7/6DUQuzk54avkd+HHbPmzdfeCKY51yuQwajWVuwnZ0dMDYUSkYnTIcBapifLn+e4NujUD3mOij9y5BpDIEMPO4rL1SVQi7Njoo5Ajw9RQpG8uQSqVQBvkhr/jS2G9xFYsbxeLt6YaFM8ZiYuow7Mo8jexclcFnR1t7J9ZvzcCv718EJ0dOyktERNevM7n5guVhsdEiZUJERETXk3vvvRfNzc19rr/4d3x2djZycnL63K6/zF1HIQYWNxIR2bgxKYmC5cNZ2T2/gDbsOILtB08b7DMkKgJ3LVuEAD9fq+R4kVQqxYzJ6WhsaEDu+XNoawOqq6uh0WgQFRWFzTv3Q6fXY9HcaVc9lrOTI+5YPB8jhw/Dqq83ot5IJzYA6OpS95zbw90NGo0WLT93jOwPmUyGkKAARChDkTQsDhqNBnsPZGLDD1v73CdtZBKWLpwLVxfnfp9vsFOVCYsbIwZhcaODQoHgQH+Ullf2xIpKKljcKDKZTIbRI0cgZXgCjhw/hX0Hj6Clta1nvV6vx4nTZzA5fYyIWRIREYkv55ywuDEhfohImRARERERERERAWNShwuKGw8dO4mH7roVWq0On6zbimPZFwz2iYuJwl1LF8HP19uaqSJCGYrf//pR/PbPf4OTkxNaW1tRXV0NhUIBNw9PvPLuSvz6kbvg5+N1xeMo5HIsnDsVyYlxWLl2I8qraoxud7GwUS6TQSaXobOzy6T8pVIpwkKCkD4mFYEB/jh0JAtvfvAp1BqN0e1HJydg6U2z4ebqYtJ5BxtVRb1gWRnkZ5VJh60tPFhY3Kiq6vsGPbIOX28PLJ6djkmjErDr8GmcySsWrO9Sa3D6fBFGD48VKUMiIiLxnT0vLG4cyuJGIiIisgJPT080NRmveZBIJEYbRJmjMFEikdh9gSOLG4mIbNyYlATBck1dAwqLy3BOVWO0sHHB3BmYM32SaAMnbW1tOHfuPMKCA1FRUQ6NRgMnJyfU1tYiMDAQW3dlwMXJEbOnpl/T8YbFRePPv3nkql0cdTodGn4ugPTz9YGvtxfkctnPxY96aLRaqNUaSKVSKBRyKBQK+Pl4IzwsBH6+3lCr1Sgtr4SqpBxffP2d0Y5vF3m4ueK2W25AStLQfj0314uOzi5UVtcKYuGDsLgR6H5clxc3qsoqr7A1WZNcLse4tJFITU7ENz/8JCjgOH4yG5PGp121iywREdFgZti5kcWNRERERERERCSetJFJWP3Npp7lzGOnoNPp8cX3Ow0KG6VSKW69aR4mp6eJNiZaUlIKTzcXBPp5I7OoCHq9HgqFAjU1NXB0dMQbK77A04/eA0+Pq3dxjFSG4PdPPoAft+3Dll0ZfW6n0Wqh0XYXOoaGBMHT3Q2dXV2oratHQ6PxgjOpVIqgQH+Eh4YgPCwEgf6+0Gi1KK+oQsbhY1CVlPV5Pnc3F9y+aB5SkuKv+hiuR6pKYefG8ODB2c1Q2etxqaqN36BH1hfg64Wl8yaivKoO3+3MREX1pdfk8TP5LG4kIqLrmkHnxjgWNxLRICaRgLdCAuBzQDZAqVSiuLhYcH/yxaJDey8+tDQWNxIR2bhIZQj8fLxQU9fQE1u5YRsqW3SC7SQSCW5fvAATx422coZC+zIOobOrCwqFHD5eXpBJZdBLpNBoNCgoKEB8fDy+2bwT7m6uGD86+ZqOebGL48RxqdidcQSZx0/3DNwZU1Nbh5raSxfuJRIJAv394OrqDIVCAZlUCo1Wi7KKSuSczUVdQ+M15SGRAGkjh+PWBZyZ9ErqG5vQ+/tXSHCgOMlYWGivx1VXf22vJbIeBwcHTBg7SlDcWN/QhKLiUkSGs8smERFdn+rqG1DZqxMAixuJiIiIiIiISExjRg4XLJ8+ewFffb8TB4+fEcTlcjkeunspRiSKNwmpXq/H9l17oNVq0dLSCmVoMCqqauDm5oaysjJ4eHgAAN74X3eBo4uz01WPebGL48ikeGzemYETOecMxtsuV1pWgdKf/18ikSDA3xdhwUHw9fGCTCaDo4MjZDIJdDo91BoNyiuqsGNvBmpq6/s+6GXYrfHqquqEBaXKoEFa3NjrcTW0dKJTrYWjQiZSRtRbcIAPpo0dji837u6JlVXWoqq2AQG+XuIlRkREJBK1Wo3zeUWC2DB2biQiIiIrcHd37/l/vV6PhIQEJCUlwcPDA15eXmY/X0NDA5qamrBmzRqzH9vaWNxIRGTjJBIJxqQk4scd+3ti3/y0D+PHCzsf3r10EcaljbR2egJNTc04fPQYACAoMAB19Q0IDPCHm7s7TmafRXNzM8rLyxESEoLV325BbFQ4/Hy9r/n44aHBuHvJAtwyfyYOHDmBHXsPoaHJ+Cykl9Pr9aioqh7w43J1ccaEMSMxcVwq/HyuPd/rlVqtESzLZDI4KBQiZWNZTk7CwWi1RtPHliSmkOBA+Pv5oLrmUtFz1qkcFjcSEdF168w5YbcDuVyOmKhwkbIhIiIiIiIiIgJSRyRAJpNB+/MEpzqdDms3bkNISEjPNnK5HI89eCeGxsaIlSYA4HxuHkpKyyGXy5EwNA7HT5zCpPSxUJWWo6m5Bfn5+UhMTER5ZQ3WfLcV9y276ZqPHR4WjIfvXoz6xibsPXgMuw8eRXt75xX30ev1qKquRVV1rUmPSyaTYmTSUEwZPwoxkUqTjnU96Ow1JurmevUiVnvk7upsEOvSsLjR1gyJCIa7qzOaW9t7YllnCjB7orj3kBAREYkhr7AYml73cLFzIxEREVmDUnnpmtry5cvx9NNPW+W8c+fOxQMPPAAAaGlpgZubm1XOa05SsRMgIqKrGzMyUbB86tRpHD58GGq1GgCwZNENohc2AsCe/Qeg0XQPOEokEgyNj8WIpAQoQ4OREN/dCaasrAwtLS3oUqux8uuN0On632LZ1cUZMyePw9//8CSeePAOTBgzEj7enmZ9LAAQFRGGe29biH/+6VdYdMMMFjZeo97FjQrF4J1Lofdj6/3YyTZIJBKMHJ4giGWfzUVn55VvBiAiIhqszpzPEyzHxURBMUgnoyAiIiIiIiIi++Di4ozhw2IFsczMTDQ0NAAApFIpHrp7qeiFjXq9Hjt27+1Z9vBwx/CkYVCGhWBMajJcXZzR1dWFwsJC6PV6HD5+Gidzcvt9Hm9PD9w0Zyr+86df4cE7FiEmMgwymWVu8fHx8sBNc6bgH79/HA/cvoiFjddI/fO4+EUK+eAcE5XLDYsY1RqdCJnQlUilUgyPjxTETp4r6CkYJyIiup6cOZ8vWA7094WPt5c4yRAREdF15fLujI888ojVzpuenn71jWzc4LyyRnTNJCbs2/+CLOGpTTi3Kac25SEDMKkm2pTHDAB6E/bXmXLB1sQZB+WOA99X35332NQkQVir1eLo0aM4e/Ys7r3rDkxJH2NKhmZRX9+AY1knBbGxo1MxIikBH6/8ElERSrS0tkFVUoa8vDwkJiYit0CFPYeOYmp6Wq+jXdvPWiqVYFhcDIbFdQ9iajQaHD91FtnnLkBVWo6GxiZ0dHZd07EUcjnCQgIRHhaM8LAQRIeHIjDA75r2tXsS8w6E9q5XlZj63rdh0l7PnVZ3jQN5ehM+yAfx82lJIxKHYtvuDOh+/hmp1Rpkn81FanLSVfYkqzPl/WH6yUU89/VHb+LPWryPQ1NfJ6b8HWDq3yAi7Wzy+/o6fG9a+Dk7c17YubH7+7S5nmexfl4ivjeJiIiIiKj/eJ3Tfoh6vU7E14lY11FM/vtWxPEHU14rP4/lpo1MQtbpsz3hsrIyrF69GsOHD8c///onjEgcalqOZpCdcxaVVdWC2AN334Gsk6dRqCrGmFHJ2HvgCOrr61FTUwN/f398sWETYqIi4OpyeQe8a3u+5AopRiUnYVRyEnQ6Pcorq7D30DHk5qtQW9+Ari51vx+DXC5DWHAglKFBSIyLQdLQIZBK+zFeaNJrZeCvE4nUtHF7vcKwA2E/zm4Q0WqFj8VSxadikxl5bWj1EkB6tVvOTCmANPG57M/r2dznNoWJn8MjE2KQcexMz3JrWwcuFJUjPjrs6jub9P4yIW8z36tgP0z5fW2nXVNNHuOy178fRL3JTxyijmeacG6T/+yx1/FMEfM25bVylX1zzvUaC42NNt/ftqL+jUxERES27mLnRg8PD6t3T1QqlSgpKbHqOc2JxY1ERHZg+oQ0xA+JxLkLhYJ4a2sr3nn/Qxw5ehSvvfh3jB/bu0jQenbt3d9TNAR0D4pNnjAeHh7umDIxHbv27sewuBhU19ShvaMDRUVFiI6Oxjc/7kBS/BD4+ZreFVEulyNtZBLSRl4qVurs6kJJWSWqauqgVquh1mig1Wohl8uhkMvh5OiAkKAABAX4dw842em1QVuiUAgvpms0g3c2SLVG2KnRYRB3qbR3bm6uiI2JwLncgp5Y1qkzLG4kIqLr0ukz5wXLFycLISIiIiIiIiIS05IFs/Hhyq8FMZ1OhxMnTuDeB3+Bv//lD3jovrsgk4lT2KHVagVdGwEgwN8PI4YnIjoqEu9++DEAICF+CE5mn4VKpeq5iWnt91tw37JFJp1fKpUgNDgQty2a13NTtVqtQWlFJQqLy1BUUo7WtnZotVqo1RpAIoGDQg65XA4vD3coQ4MQERqM4EA/0Z7DwUQulwGXFZcO1jFRrdawSFE+SAs57Z2ftwfCgv1QUl7TEzt+Jv/aihuJiIgGkbO5ws6NQ+OiRcqEiMhKJBLe+wzwOSCb4OHhAQDw9PQULYeGhgarF1aaA+/AJyKyA3K5DHffcRu+Wr8R2dnZBp2ODh85hvTpN+COpYvx7789C2VYqFXzq6qqxsnTOYLYmFGp8PBwBwBMnjge+QWFUJWUYkRiPA4dPYG6ujp4enrCz88Pn6/7AU8uv9MiHf4cHRwQE6lETKTS7Mcm4xQKhWBZrVZDr9cPyg6OarVwNlyFXNHHlmQLRg5PFBQ3qkrKUFNbBz9fHxGzIiIisq51323G1p3Cm/CGxQ8RKRsiIiIiIiIiokvSx4zEgvk3YNuOnWhvbxesq6mtxaNPPoO33/8Ir734d0yfOsnq+Z04eRp19Q2C2PQpkyCVSuHh4Y6FN87DV19vQFhIEMorq1BdU4f8/HwMGzYMh4+dwqgRCRg+LM6sOSkUckQqQxGptO74MAEKubBAVD1Iixu7ek32Chg+drIdKUOjBcWNuYVlaG3rgKuLk4hZERERWc/Z3Hx8t2WXIJbAiV6JiIjISpKSkvDQQw8hPDzc6udevnw5VCoVvLy8rH5uc+BUWkREdqCgpAql1Y2YNGkSli5dirAw4zPrfbFmHeJTxuOv/3wJbW1tVstv5559goJLBwcFJqaP7VmWSqW4ZeGNcHJ0hJ+vD8LDQgAARUVF6OjowLm8QhSVlFktX7IsV2fDgZHaunoRMrG8mlrh43JxdhQpE7oWsTGRcHVxFsSyTp0RKRsiIiLrO51zDvf+8reCmKOjA2ZOSRcpIyIiIiIiIiKiS/YdyUZYeATuuOMOpKSkQCo1vKXlVHYOZsy/BTffdi8u5OUbOYplaDQa7NqbIYiFhgQjPu7SpFFD42MxOjUFEokEIxKGQi6Xo62tDaWlpQCA77fuMpjEluyXo4ODYLmxxXrj89bU1Gz4uBwULG60VUlxEVAoLvU60Ol0OHmuULyEiIiIrKixqRmL7n0SzS2tgvikcakiZURERETXG3d3dzzzzDNYunSp1c+9dOlSPPPMM3bZtRFgcSMRkV3Yc/Rsz//7+Pjg7rvuxLovP0HskGiDbdvb2/H8P15EfMp4fLF6ncUHyErLynHmXK4gNn5MGlxcXAQxLy9PLLhhDgBgWFwMnBwdodPpkJ+fD51Ohz0Hjlo0T7IeTw93uLkKf/6qQVq8qiopFSwrQwJFyoSuhUwmw4jEoYLYidNnoNPpRMqIiIjIeuobGrHo7kfR2iq8GemVv/0RAf5+ImVFRERERERERNRNp9Nh75EcAICDgwPGjx+PP//x/3DzghuMbv/N9z8iYdRE/O5Pf0VjY5PF8ztyLAtNzc2C2IypkyCRSASxOTOnwd/PF05OjkiI7y58rKioQGNjI0rKKlGoEo4tkf0K9vcWLKvKq0XKxLJUFTWCZT9PFziwc6PNcnRQYGi0cLLsrDP5LKwmIqJBT6fT4e7Hfo/zeYWC+N1LFiC5171CRERERGR7WNxIRGTjmlvbcfxMgSA2beI43HLTfJzO3ItX//03eHp6GOxXUlqGOx94FOnTb8DhI8cslt+OXXsFy85OThg/drTRbRMThmJk8nDI5XJEKEMBAK2trSgrK8ORE9loaR2cs1lebyQSCcJDgwSxwVjcqNfroSotF8SUvR432Z6U4QmC5eaWVuQVFImUDRERkXVotVrcsfxXyCtQCeL333ErHnvwLpGyIiIiIiIiIiK6JPuCCnWNwuLB++5YgvVffYrtP6zHiKREg33UajVeeu0txCWPw4cfr4RWq7VIbp2dndi7/6AgFhmuRFRkhMG2CoUCty5aALlchrCQIDg7OwEACgoKoFarsfvgEYvkSNYXHiycMKy4vKaPLe1b76LN8ADDexPItoxMEE6SXVXbgPKqOpGyISIiso6/vvwONv60WxAbOXwY3n3xOZEyIiIiIqL+YHEjEZGNO5B1Hhrtpa5icrkc48ekAuietfTXTzyK3BOH8Ivl90MqNfxYP3j4CMZOmYN7HvolSsvKDdYPlF6vx+nsM8grKBTEJ6aPhZOTU5/7zZs9A74+3lCGBkP680ym5eXlqKurx4EjJ8yWH4mrd5HfYCxurKmtR3t7hyAWzs6NNi8wwA8hQQGC2PGTOSJlQ0REZB3P/vO/2Lx9jyA2JjUZ77z8gkF3ASIiIiIiIiIiMezJzBYsK0ODERne3X1s+tRJOJaxHe+98TL8/HwN9q2qrsbDj/8GoybMwK49+82e24FDR9DaJpykdca0yX1eVwkMDMCs6VMhkUgQERYCoLsQs6CgAEezOOHrYNG7uLG0sg7ay8b1B4veRZtKFjfavMjQQHh5uApix8/ki5QNERGR5X3z43b87ZX3BDE/X2+s//h1uLg4i5QVEREREfUHixuJiGyYTqfD3mPnBLHRKUlwc3URxPz9/fDOay8i68BOzJg62eixVn65BnHJ4/C3f7+C9vb2Aeek1+uRl1+Ajz/7Al9/871gnbubG8aMTr3i/g4ODli8aAFcXJwRdFmBUX5+PnbsPQidTj/g3Mh2hIcGC5ZVJeXQ6wfXz7Z3waabqwu8Pd1Fyob6Y+QI4ezO5y7ko61t4J+LREREtuzrbzfhX/99VxAL8PfFuk/fhpOTo0hZERERERERERFdUl3XiDMXVILY5PQxguJBmUyGRx68F7knDuHpJx+DQqEwOM6JU9mYNm8RFt9xH/J7TdA6EMUlpVj5xRrs2issmIwbEgNlWOgV9x0zOhWxMdHdE77+PEFtY2MjysrLkZGZZXJuJD5lr+JGjVaLsurB1R1Pq9WhtFL4mMIDPEXKhq6VRCJB8lBh98bT54ug0Vimuy0REZGYcs7l4Z7H/yCIyWQyrPnwFUQoQ0TKiojIyiQS/rv4j4jsFosbiYhs2AVVJeoaWwSxyelj+tx+eFICftr4Nb5dsxJDYqIM1re1teG5v/0b8Snj8dXaDf0qNrtY1PjRZ59j5ZdroSopNdhm8sTxRgcSewsJDsKMqZMQobw06KdWq3HkWBbyClVX2JPsRUSYsLixrb0dRcWDq3vjmfMXBMvhoYHsfGQnkobFQS6X9SxrtTqcyjl3hT2IiIjs0+mcc7jv8d8JYnK5HF9/8jbCek1GQUREREREREQkloNZZ3H5qKWzkxPSRg43uq2Xlyde/tdfkX1kL26aP9foNuu//QHDUifg98++gKam5n7noyouwWefr8b/Pv0ceUaKJKdPmXjVY0gkEixaMA8+3t4IDvTviRcXF2PLjr39zolsj6uzE/y8hROf5hVXiJSNZRSVVUGjFRbEKf3ZudEepAwT3i/S0dmFs/klImVDRERkGQ2NTbj5vicNOqO//PwzmDqh73ssiYiIiMj2sLiRiMiGFZZWCZZDgwMFBYHGSCQS3DR/Lk5n7sXL//wrPDwMO8kVl5Ti9vsexsSZ85F59PgVj6fX63EhLx//+3QVVn65FsUlxgvUYmOikZoy4iqP6JLxY9MweuQIQRfKhoYGbNm+55qPQbbL29MDPl7CWTv3HswUKRvza2tvR+bxU4LYkEilSNlQfzk7O2FobIwgdvrMeZGyISIisoy6+gYsvOtRtPYazHv9X89i0vg0kbIiIiIiIiIiIjJ0QVUuWB47OgUODg5X3Cd2SAy+XbMSW79bi8RhQw3Wd3V14T+vvom45LH436eroNVevWNZkaoYn32+Gh999gXyC4uMbjN2dCqCggKveiwAcHV1xc033SAY39Xr9cg8egw1dfXXdAyybZGhwtdCxrGz/Zpg2NbtP35WsBzo7QoXp6tPdkzi8/JwQ2SY8PV54lyBSNkQERGZn06nw92P/R65+cLv7fcsvQlPLr9LpKyIiIiIaKBY3EhEZMOKymsFyzFREdfcGc7R0RFPP/UYck8cwsMP3AOp1PAjP+NgJsZMno37Hn4cZeXCWST1ej1yL+RhxSersOqrr1FSWm6wPwB4enjgxnmzcduSmyGTyYxuY4xEIsHNC+YjKDBAEN++ay+qqmuu+ThkmyQSCSaMSRHEjhw7iba2dnESMrODR7KgVqt7lqVSKcanJomYEfVXdK9i1Na2tj62JCIisj9arRa3P/Qr5Pfqiv7AnUvwiwfuFCkrIiIiIiIiIiJDOr0exeXCscFhcTF9bG1o1oypyDq4E++89iJ8fX0M1ldWVeOhx36NtEmzsGdfhtFjFBap8Omqr/Dxyi/7LGp0c3XF3FnTMXf2jGvODQBioqMwb/YMwThqe3s7vlr3Xb+OQ7Zp7IhYwXJpVR3yiitFysa8Wto6cDQ7TxAbMzREpGxoIBKHhAuW6+r738mWiIjIVv3lxbfxwzZhE4VRyQl498Xnrvn+SiIiIiKyHSxuJCKyYb0H8sLD+j9YEBDgj/fffAXH9m/HtMkTjW7z6eerEZc8Fv948VW0tbXhfG53UePnq9ehtMx4UaOXpwcW3DAHTz62HKNTU/pV2HiRu7sbFtwwRxCra2jEpq07+n0ssj3paSmQyS591VBrNDhw5MqdQu2BXq/HnozDglhKYiw8PdxEyogGorJK+PnqZ+SGByIiInv12VcbsHXnXkFsTGoy3n7prxzMIyIiIiIiIiKbUlXbgM4utSDW3zFRuVyOXyy/H7knDuHXjz8KuVxusM3xE6cwZc5CLLnrART8XMBYWKTCJyu/xCervkJBkcpgH+BSUeNTv3wY48aMHtC1lVnTp0AZKnxMGYeOorCouN/HItsyNDoMft4egtjeI9kiZWNeB0+cg1pzqeOpTCpBemKYiBlRfzW2CCd3dXdzESkTIiIi88rIPI5//Pd9Qczf1wfrPnodzs5OImVFRERERKYwvKJLRNdIxBtCTTm13o7PbcrJpf0vvOuh0159mysa2Llb2jpQ29giiA2kuPGi5BFJ2P7jenzz/Y945o/PI7+gULC+tbUNf/7rv/Dqm+9h1vQpGBoXa/Q43l6emJQ+DskjkgZU0NjbxPFj8PmaDaiqqgIAtLV3IDe/AF1dXXBwcDD5+P1myuvUlPeHqR8pJr2/TH5zGuXp7oaRSUNx5EROT2xvRiamTRxntJOovTh3IR9V1cKuqpPHjhQpG3timdfZNZ1Zb3huVWk59JflFBYSZHQ7AHZcBCLecy7eqU07cV+vAbJN9vvzEi9viUnzG4n5fIt5brF+B5j2mNdv3CxYDgzww7pP34ajo8M1vHfs9b1FZCWm/P6x16+VRERENIjZ6xcU/t1iVXZ7fdRUJjxuUf9uEClvYMCvFVWvyV49Pdzh6eE+oGN5e3vh1f/8DY88eC+e/sNz+GHzTwbbfL3he3z3wxbMmjYZI4Yn9jke6e7mhonpY5GaMgIKhWJA+Vwkk8kwb/ZMvP/Rp9DpdACAxqZmnDidg8iI8Kvs3QeTxu3F+Vl3M+UapalvEPPfbyAFMGl0Ijb8dKAndvxMARa3tMHDjgvJdHo99h7NEcRGxgbD3c1VpIz6SyfSviKOgUsMz11cUSt4vypD/I1u9/MBBn5uE+7HkUhMvOfElM8jUb/jmPL72pTXqIiu2++Udsqk7wpi3pxoj+cF7Hcs1MS8TXidffLVN4JluVyONR++jPDQwKt/Tpp8n4FYNxcSWQHHQYmISET2W1lARDTIFZdXC5blcjmCA/1NOqZEIsHNN81HztF9+M/fnoO7u2Gnubq6eqz++hvkXsgXxL29PLFw/lw8/uhDSB2ZbJbCRgAICQpAZESEoHiooakZlVXVV9iL7MXkcaMEy1U1tTh09IRI2ZhOp9Nh4xZhZ9HgAF/ERilFyogGoqurCxW9PmOUocEiZUNERGR++YXCWf9f+MOvEBoSJFI2RERERERERER9Ky4z//X6+Lgh2LjuC2z+ZjUShsUbrO/q6sIPW7bh45Vf9hQbXuTu5oZ5s2fgqV8+jLFpo0wubLwoMSEewcGXHltjcwsqKjkeOhiMSxkKhfzS2LlWp8OOg6dEzMh0x3PyUVPfLIhNSo4UJxkaEK1Wi7JK4YS9YYF+ImVDRERkXrl5RYLlp39xL6akjxYpGyIi8UkkEkgkUv7jhBpEdo3FjURENqq8ul6wHBYcaLaCQkdHR/zuN08g98QhPHTfXUa/0J3OOQMA8PH2wqIb5+HxRx/CyJQRZsvhIrlcDmVYCJycnHpizS2tHMwbJGIilQjpVZT79beb0NDYJFJGptm576BBscCksSP5R5GdKauoEtysIJVKERIUIGJGRERE5qPX65FfJPy+MjQ2RqRsiIiIiIiIiIiurKRS2LkxPCzEbMeeM2s6Thzchbde/Td8fLwN1ldUVqGwSAUA8HB3xw1zZvYUNcrlcrPlAQARyhC4uV2aeLazswulZRUGxZVkf9xcnDAqaYggtv3gSRSV2ed4d0tbB9ZuyRDEQv3cER1s+B4i21VZ0wC1RiOIKYNZ3EhERINDvqpEsJyWkihSJkRERERkLixuJCKyUZ1dasGyp6eH2c8RGBiAD9/+L47u24ZxY4SzFzU2NePmBTfg8UcfQkrycLMXNV7Ow90Nzs7OPctarZadGwcJiUSCm+ZMFcTaOzrwxdffQa/Xi5PUAFVW1+C7H7cJYn4+XkgfNVykjGigikvLBcuB/n5wcHAQKRsiIiLzqqisRkdHpyAWHcEu00RERERERERkm9rbhdcx/IwUIZpCLpfjl488iNwTh/DkL5YbjHk2N7dg/txZePKx5RgzOtXsRY0X+fp4C8ZDge4xs5raOoucj6xrxvgUSKWXbsHS6fVY+d0uqDVaEbMamDWb96O5tV0Qm5U2hJO92pniCmHhuK+XB1ycnfrYmoiIyH50dnahpKxSEIuOCBMpGyIiIiIyFxY3EhHZqC61cBY9hYUG0gBgZMoI/OUPzwhiDY2NGDE8UTAIYykOCgVcXFx6lrVaHSqqqix+XrKOEQlxSEtJEsROnzmPQ0eyxEloAHQ6HVat/sZgdsu7F8+Dg4NCpKxooHoXNypDg0XKhIiIyPzyC1WCZUdHBwSzQzERERERERER2ajexV8KhWXGXXx8vPH6y//EHUsXC+JBQQFIGzXSYkWNF8llMjg4OAgmW9TqdJzwdZAICfDB3Empglh5dT027TkqUkYDk3WmAEez8wSx4dGBGBVnvo6qZB0lvYobw9i1kYiIBomikjKDCfWjwkNFyoaIiIiIzIXFjURENkqjFQ7kyeWW65wIAHGxMYLlxsYm1FppplCZXCYobtTpdKisqoFOp7PK+cnylt40Gx5uroLY2m83obauXqSM+mfb7v3I61UoMHV8KmKj2AXJ3uj1epSUVQhiLG4kIqLBJL+oWLAcFa60yoQlREREREREREQDYVjcaNkiw7jYaMHyhbxCi57vIolEArlMJujeqNPpUFHJCV8Hi7mTUhEWJCwg+ynjBIrK7ONn3Nzajq827RPEXBwVuG3GcHZttEMl5cLiRmWQv0iZEBERmVd+UYlg2dfHC54e7iJlQ0RERETmwrvbiIhslFwmLGbsXexobhHhSoOZUM9fyOtja/PSanWC4kapVAq1Wo36+garnJ8sz9XFGbffPE8Qa+/owBsffIrGpmaRsro2h46ewDc//CSI+ft4YeGcySJlRKaoq29AW3u7IMbiRiIiGkzyC4XFjdGRnIyBiIiIiIiIiGyXTCosmrL05KdxQ4QTvubm5Vv0fJfT6fWCMVGJRIKKSnZuHCxkMhnuWjhNMNGYTq/H+6u3oqa+ScTMrq6jswvvfrUZza3CMbRbpybC09VJpKxooJpa2tDQ3CKIKdm5kYiIBonexY3R4WEiZUJEZEMkEv67+I+I7BaLG4mIbJSiV6dGtVpj0fPJZDIMiYkSxM7nWqe4Ua1WQ6FQ9BRXyn4e8Kmo4mDeYJKcGI+0lCRBrLqmDm9++BlaWltFyurKsk7lYOXqDQbxuxbPg6ODgwgZkamKS8sFy+5urpzBjYiIBpX8ImG36agIFjcSERERERERke2Sy4WdGi09Jtq7uFFVXIL2XpMiWoJOp4NWqzWY8LWymuOhg4kyyA9zJ6UKYo0tbXhj1Q9oaLLN8dAutQbvr9mKojLha3F4TDDShoaKlBWZoqRC2LXR0UEBfx9PkbIhIiIyr/yiXhO9RrC4kYiIiGgwYHEjEZGNcnAQdlFsabH8YEfvwTxrdW5saW0DADg7OwPoLrQEgIrKKqucn6xn2cI5CAkKEMTKyivx6tsfob6hUaSsjDt0NAsrVq4xmCF4waxJiI1ikYC96l3cqAwNhoQz9hAR0SDSu3NjFAf0iIiIiIiIiMiGKRTCCV/bOzoser7ek73q9Xrk5Rda9JzApcd1eXGjTCpFS0urVcaByXrmTkpFTHiQIFbb0IxXP/3O5jo4tnd24Z0vN+F8YZkg7uXmjNtnjeQYmp0qLhcWqoYG+vFnSUREg0ZBr86NURGcjIGIiIhoMGBxIxGRjQr09RIsl5RXGBRZmVtsTLRgOTcv36LnA7pnKS0tqwBwaTDP1bW7yLGSnRsHHRdnJzzxwG3w9/UWxCuqqvHK2/9DQa/ZtcSg1Wrx40+78OmX6w3eczMmjsbcqeNEyozMwVhxIxER0WBiOFtpuEiZEBERERERERFdna+Xh2C55OdxQ0txc3NDaIhwbMAaE75efFyOjo6QSqWQSiRwcnQEwAlfBxuZTIZHb78BYUF+gnhtQzNe/eQ7qMptYwy8oakVb6z8AblFwrEzN2dHPHHrRHi6OomUGZmquFfnRmWwXx9bEhER2Z/8XsWN7NxIRERENDiwuJGIyEaF97rA3NnZhaqaWoueMy62V+fGXMsXN1ZU1aBLrQZwqbjR093953W2MbBD5uXp4Y4nHrwDPr0Gq+vqG/DyWyuwfuMWqH9+TVhbWXklXnrzQ2zcssNg3cS0ZNwybxpntbRjHR2dqK6tE8TCQoL62JqIiMj+tLd3oKy8UhCLjmTHaSIiIiIiIiKyXeEh/oJlVUlZH1uaT+wQ4YSv53MtX9x48XFJJBI4OzvD3c0VUmn3mFNlNcdEBxsXJ0c8ftd8gwmNG1va8NL/vsHGXUeg0WpFyU2v1+NA1jn8/b21BoWWzo4K/HJxOoJ83UXJjUyn0WhRUSUcD1UG+/exNRERkX3R6/UsbiQiIiIapFjcSERkozzdXeHp7iqIWXowL26IYedGS3eLVBWX9vy/s7MznJ0c4eigAAA0NTWjra3Noucncfj5eOE3j96LQH8fQVyv12Pbrv3453/ftWoXR61Wi83bd+Pfr71n9H02c1Iabl80m4WNdq6krBx6vb5nWSaTITgwQMSMiIiIzKtQVWIQiwrngB521E55AAEAAElEQVQRERERERER2a7wYOF1+rLySmg0GoueM25IrwlfrdC58fLxJxcXF3h6XCoeq6hkceNg5O7qgl/dtxChgcLxUJ1ej017j+E/KzZAVV7Tx96W0dDUine/2oxV3+9Ge2eXYJ27iyOeWjoJ4YHeVs2JzKu8ug7ay+7xkEgkBl1EiYiI7FVdfSOaW1oFsWiOhRIRERENCnKxEyCigTChuMbkuhxTDqC/+iYWY8K5JSbmbcL+4cF+ONV86Q9yVUkZxqQmm5bPFfQeyGtvb0dpWTmUYaEWO6eq9NJAnrOzM7y9PAXrK6tqEBUZbrHzDxp2+Nb08fLAbx6+G+99thYFxcKCwsqqGrz81gpMHDca0yaNQ1CAZWaT1Gq1OJlzDpu37UZxabnBeolEgptmT8LsyWNtp7BRb6efpaae2QyPu6SsQrAcEhQAufzqX4fNcW4xiPuatc/nzDTX42MWm1ivcfv9Wev1lp20YjAS66N0oL978gpVgmV/Px+4u7uiX69bMX/v2cr3LaIr4euUiIiIiIjIrHp3FNNotSirqEJ4WIjFztl7TDQ3L99i57qod3Gjh4tDz3JFZZVFztnU3IKS8kq0tXVArVZDrdFCKpVAIZfDQaGAj7cHQoICoLiGsRIaGA83F/zqnhvx3ldbkFdcKVhXVlWHl/63AbPSkzF93Ai4uThZLI8utQYHT5zHdzsOGxQ1AoC3uzOeuHUiAn3YsdHeFfcqmPX38eqZXJqIiMje5feaKF8mkyEsJFCkbIiIbIdEIoFEynHs7qF8+72vi+h6xyuUREQ2TBnsj1Pni3qWLd3JLigoEG5urmi5bIaj87l5Fi1uzC+89JgkEgkilMKByorKKhY3DmLubi74zSN346e9B/Hjtn3QaLU96/R6PfYeyMTeA5mIHxKFyeljMCJxKGQymcnnbWxqxv6DR7Dv0FE0NDYZ3SbQ3wf3LL4BUeGWGzwn6+pdwKoMDRYpEyIiIsu4/Ls1AERHKEXKhIiIiIiIjJPAYLIeUydPuC7v1bDXG3Wuyx/W9cmU97WpLxOTPlPEeY26uDjDz8cTNXWNPbGi4lLLFjfG9urcmGvZ4sa2tnZU19T1LLu4uMDT3blnubauHmq1GgrFwAuQWtvaUaAqhaqkDKrSCqhKytHQ1HzV/WQyKUKCAhAeGozw0CBEhIUgLCQQUql0wLlckcSE40pNHCPUm3CLlG7g3URdnBzx5N3zsXlvFrbsPw6d7tJ7TafXY8v+LGw/eAqjEqMxaXQiIkP8zTaBZVVdI/YdPYMDWefQ1tFpdJu0hHAsmZ4CV2cHg3USE24rM+0TxcTPI50JE/2Z8LOG1PA57BdTJp77+SVTXCHsBKsMHuRdGyUmfC6Y8j4z+T1qws/6ev1KaervgAEz8Qk3affr9YdtAr326tsMNiZ/Hon49/UAJubtPRYaERYMuUzWz9+h9jqpvL1eCyGrE+s7jrF9OWksERH1A4sbiYhsWGRogGC5oKgE5RVVCA4K6GMP00gkEsQNicGxrJM9sfMX8jBj2mSLnK+0vFIwSykAxMVEoaamtme5oqq69240yMhkUsydmo4Rw2Lx2dqNUJVWGGxz7kIBzl0ogKeHO0aPHI5IZRgilCHw9fG+psG9LrUapWXdA7nnL+TjRPZZ6PoY1JJIJJg5KQ03zpgIhYJflQYLnU5n0LlRGcLiRiIiGlzyi4SdG6MjWdxIRERERERERLYvIiRAUNyYeewkJo1Ps9j5endurKquRkNDI7y8PC1yvszjJwXL7m5ucHe/1KVPp9OhuqYWIcFB/TquXq9HboEKew4cRdbpvse+rkSr1aG4tALFpRXY/3PMx9sTE8emYkJaCtzdXPt9TDIkl8lw49RRSI6PwMrvdqO0qk6wXqPV4tDJXBw6mQtlkB8mpA5FbHgwAnw9+1VoqtfrUdfYgsLSKhw4cQ5n8kr63NbD1Qm3z07FiCGc6HWw0Ov1KOnVuXHQFzcSEdF1JV8l/G4THREmUiZEREREZG68Y5+IyIbFRYXBzcUJLW0dPbE9BzKx7Ob5Fjtn7JBoYXFjbp7FzrUn47Bg2cPdFSOHJ+KnnXt6YpUsbrxuhAT647e/uBfb9h7CD9v2Cro4XtTY1IztuzN6ll2cnaEMC0ZYSBBcnJ2hkMshk8mg1qihVmtQ19AIVXEpyiurr2lAl90aB6/qmjp0dnUJYmEh/btJgIiIyNYV9JqtNIqdG4mIiIiIiIjIDiQPi8HR07k9yxcKilBaXonQ4ECLnC8qMhwymQzay8aicvPykTZqpNnPpdfrDcZERyTGo6utGbV19T2xisqqay5ubO/oxOFjJ7HnwBGUV9VcfYd+qqtvxHebd+KHn3YjdfgwTB4/GtERYWbrJng9Uwb74XcPLTLaxfGi4ooafPXjPgCAg0IOZZAfwoP9oAz2g5e7KxRyGeRyGbRaHdQaLVraOlBSUQNVRQ2Ky2sE9xb05UrdGsl+NTS1oqWtXRALC2RxIxERDR75RcLixqjwUJEyISIiIiJzY3EjEZENU8hlSE8dhq37jvfEDh3JwsIbZsLJ0dEi5+w9U2luXr5FztPe0YHDR08IYhPSRhoUG1XX1EKr1UImk1kkD7ItMpkUc6aOx+jkBOw9dBwZR7LQ0tre5/Zt7e04l5uPc7mmvU6jlCGYNDYFo0cMg1zO19pgVFxaLlj29vKEu7ubSNkQERFZRkHv2UrZuZGIiIiIiIiI7EDysBi4u7mguaWtJ7Yn4zBuX7zAIudzcHBAVGQ4LuQV9MTO5+ZZpLgxN78Q5ZXCyVynpKfh5MlTguLGymsoUtRqtdi6KwNbd2UYTOjYF0dHB/h4eUGhkEMhl0On10Gt1qC9o1NwfuPn0yEzKxuZWdkIDw3GbTfPQ6SSE4Sa6mIXx5HDIvFTxkkcP5MPjdb4JK1dag3yiiuQV1xhlnMPiwzEjNGxGBbFCUAHo+Jy4WeNi5MjfLzcRcqGiIjI/AqK2LmRiIiIaLBicSMRkY2bOCoRP+07jotzNnZ0diLz2ElMGp9mkfP1Lm48f8EynRsPHz0hGHSTSiWYOHYknB2Fs0NqtVqs+/YHLLpxLhwcOHPk9cLX2xOL5k7F/JkTcfzUWew+eAwFqlKznsNBoUBayjBMHjsSyhDLzPxLtkGtViPj8DFBTBkSLFI2RERElqHX65FfJOzcGB0RLlI2RERERERERETXTi6XYcKoRGzendkTO3zsBBbNnwVnJyeLnDM2JlpY3GihMdE9GZmC5UB/X8THRKKqshI5Z8/3xE+fOYvhiUMRFmq8eLCkvBIr13yH4rK+i9wUcjkilKEIDwuBMiwE4WEhCPT3hVQqNbp9e3sHikvLoSopg6q0DEXFpaiuqTO6raq0HC+/8zFmTRmPG2ZOhkLO241MFRroi/tunobFs8fhQNY57D16BnWNLWY/j4uTAuOTIjExJRoB3ix0G6z0ej0yT+UKYmHB/uy4SkREg0rvzo0sbiQiIiIaPHi1kYjIxvl6uSMpLgKnzhf1xHbuPYj0MakW6WYYFyssbswvKIJarYZCoTDbOTQaDXbuOyiIDR8WB29PDwCAr4+3YKbQnLPnUVtXj9sWL4S3t5fZ8iDbp5DLMWZkEsaMTEJxWSWOnz4LVUk5VGUVV+zoaIxEAgT6+SI8NAjREaEYPWIoXJwtMyBOtmXvgSOoa2gQxOKGRImTDBERkYVU19ShtbVNEGPnRiIiIiIiIiKyFxNHJ2HLniPQ67unfO3s7MLBzCxMmzTOIueLGxKDTVu39yznXsg3+znqGxqRdSpHEJs0bhQkEgmCAwME8dbWNny8ajVunDsTI5OH98S1Wi227NyPTTv2QttHd78AP19MTk/DuNEj4eLifM35OTs7IW5IlGDMpFBVij0Zh3Ek6xQ0Go1ge51Ojy07M3Ay+zzuXnoTuziaiburM2ZPSMHM8SOQfaEYh07moqCkCg3NrQM+pouTAyKCfDBqWBhGxSvhoDD/fQVkW46evoCSCmHnxphwdugkIqLBQ61WQ1UqnOgjOpzFjUREAACJtPsG2eudRA/A+LUTIrJ9LG4kIrIDk9OSBMWNFVXV2LJ9D26YPc3s54qNiRYsa7VaFBSqDIoeTbFp225UVdcKYlPGj+r5/2mTJ2Ddtz/0DF4CQGVVNT74ZBWWLFqA6KgIs+VC9kMZEtjTYVGv16O+oRGq0gqoSitQXVsPtUYDtUYDrVYHhVwOhUIOZydHhAYFIDw0CGHBAXASdAbVGz8RDSpV1bXYf/ioIBYeFoLEobEiZURERGQZ+YUqwbJCoUBoMLtTExEREREREZF98PZ0x4ihUThx5lKR4cYtOzByRAK8fp4g1Zx6j32au3OjXq/Hl+u+h0536aY6hUKOcaOSAQBRkeEI8PdDVXVNz3qtVotvf9iC8ooqzJk5FTV19fjoiw1GuzVKJBKMSByKKeljEDckqs/ujP0VGR6KyPCbccuCOTiQeQx7D2SiprZesE15VQ1efudjzJ6ajhtnTTHbua93UqkUw+MiMDyueyy8qaUNqvIaFJeUoaiiDuU1jejoUkOj0UKt0UEmk8JBLoNCIUOgtwfCg32gDPRBeJAPfD1duzv26bpEflRkDS2t7dh+IEsQ8/F0x8hh5rvHg4iISGyq0grBd2uAnRuJiIjItrW0tKChoQHNzc1o6NWg5SIvLy8AgLu7O8LCru/vNixuJCKyA0NjlAgN9EVp5aWCwB+37caIpGEICzHvbHve3l7w9/NDdc2lgbTzF/LMVtyoKinDlh17BTFlSCDiYyJ7lpMShsLZyQlff7sR7e0dPfH29g6s/OprzJ4+BePGdM9qStcniUQCHy8P+Hh5ICUxTux0yEbp9Xps3LoDWq22JyaVSnHj7On8/CAiokEnv6hYsBwZHmqRTu9ERERERERERJYydVyKoLixvaMDn6/9Fo89eJfZr+vHDTEsbtTr9WY7z6GjJ3D6zHlBbNyoZLg4OwEA5HI57rtzKb7+5gfkFxYJtjt89Dhyzl9AcWUd1Gph90QACA4MwD233YwIZahZcjXGzdUFs6ZOxPRJ47Flx15s2rZbMN6i0+mxecd+1NQ14N6lN/E6lAV4uLkgKTYcSRG+YqdCNm7zvmPo6BQWss6fNgYKBW8LJCKiwSO/qESw7OXpDm8v80+CQkRERNQfLS0tOHXqFHJycnr+W1xcfPUdr8DDwwNJSUlISEjA8OHDkZCQcF0UPvIqBhGRHZBKJLjzpql4ecV66H7uZqjT6fDZV+vxf089YvbBorjYaGFxY24eMM/042o0GqxcvUEwi5JUKsVdi280GCiMiY7E8nvvxFfrvhXMWKrX67Fl+y6UV1ZhwbxZUCgUpidGRIPSsRPZUJWUCWITxoxCgD8HgYmIaPDJLxReGIuOUIqUCRERERERERHRwMRFhSFtRDwyT57riWWfzcWhI1kYlzbSvOfqNbFrc3MLKiurEBQUaPKxGxqbsPbbHwUxDzdXLJg9VRBzcXHBXbctxk879uDA4SM98dr6RmzZuR9SmQxDhgyBq6srgO5x1VnTJuKGWVOhkFvndh+ZTIYbZk3FiMShWLl6A4pLywXrj2Rlo72jE8vvWgwHjtsSWd2FonJk56qAy+63GB4fhWileSfJJiIiElvv4sbo8MF/gz8RERHZppKSEqxevRpbtmwxKGTU/1znYYrGxkZkZGQgIyOjJ6ZUKjFnzhzccMMNGDZsmMnnsEUsbiQishMRIQGYNWEktuw71hMrKavA5u17MH/2NLOeKzYmGvsPHO5ZPn8hzyzH3bRtN0rLKwWxudPSoQw1fmHdx8cbD95zO77ZuBlnzuUK1p08nYPqmlrctnghPD05CxMRCbW0tOKnXfsEMW8vT0xOTxMpI/ug1epQWVOLkvJqtLZ1QK3RQK3WQCqVQKGQQyGXw8fLA+GhQfB0dxU7XSIiukzvzo1RLG4kIiIiIiIiIju05IYpOJdfjKaWtp7Y2m83YWhcDLzMOCYYFhoCJycndHR09MTOX8gzubhRr9fji6+/Q3t7hyB++y3z4ebqYrC9VCrFnJlTERToj+83/YTq2jpkZp2CVqsDtFqcPXsWkZGRSEoYZvFujVcSFhKE3z35MLbu3IsffxJ2ccw+ewErVq3DI/csYQdHIitSqzX4cfcRQczZ0RFzJqaKlJF90Ov1aGhuR2lNIzo61VBrtdBodJBJJVAoZHCQy+Hv7YZAXw/IpFKx0yUiop8ZFDdGsLiRiIiIrOvAgQN4+eWXkZOTA8CwkFEikRg0ezLF5cdXqVRYsWIFVqxYAaVSieXLl2PJkiVmO5ctYHEjEZEdmTdlNE6dL0RZVV1P7MefdiEkOBAjhyeY7TxxQ4QzleZeyDf5mMdOnMbm7XsEsdCgAMydNvGK+zk6OmLpLTdhz/6D2Llnv2BdeUUlPvhkFZbefBMiOBsTEV1my4696OjsFMRunD2N3V57qalrQG5hCVSllVCVVaKkvApqteaa9vV0d0N4aCDCQwIRHhqIuGglHB0cLJwxERH1paBIJViOjmRxIxERERERERHZH1cXJ9x+03S8/8XGnlh7Rwf+t3INnnj4HjiY6Tq0VCrFkOgonM450xM7fyEPkyemm3Tcn3btw+kz5wWxtJQkJCfGX3G/5OGJ0Gh1ePrZf3QXNv5Mp9OhqaEeqcPjoQwNNik3U8lkMsybORVR4Uq89/EX6FKre9adPnsBn675DvctWwgpi4GIrGJ35mnUN7UIYjMnpMDVxUmkjGxTY0s7Cstroaqoh6qyHqqKOrS0dV51P4VchrAAL4QH+yA80AcRwT4I8vUw642qRER07Qp6FTdGRYgz6QcRkU2SSATd3K9bfArIQkpKSvDUU08Jihp7FzLq9XqzdG28nLG/P/V6PVQqFZ577jmsWLECf/3rXzFu3DiznlcsLG4kIrIjCrkMdy2chpdXrIfu51+Aer0eH61ai188cCcS4oeY5TxxscLiRlM7N+acy8XHX6wT/NKWSqW4Z8kCyOVXn71TIpFgysTxCAr0x/rvNqHzsoKl1tY2fPrFGsybNR2jU5N5IZloAHQ6HTQaDaRSKaRSqdlnD7G2vIIinDpzThAbPiweMVER/8/efUdHdV5rA3+mqffee0d0EEg024ApLhhjwHbcEpfkprnF3225TuLk5t6b2IntVJe4xBWwsXGh2cZUCUQTCBVQ16j3Xqad7w/BSGdGEpJG0pkZPb+1WOjsOWVP0czovGe/W6KMrIteb8DFwmIcPZWDyyWV199gBO2dXcgt7EJu4cBnhJOjA5YumIUVafMQHOA7WekSEdEYlZaLOzfGsHMjEREREREREdmoOUkxWDw3EacvDJ7rLymvxKtvf4gffPdeKJWTc6lLQnyMqLixqLjMov0dP3kGn375lSjm4eaKrbevu+62vb19+GTft4iPT0BxcTG6ugYKlgID/DB/ziycPnseLS2tuGvzbXB2drYoT0slJcTip99/EH95/V30Dul8eSYnD0H+fti4ZoWE2RHNDPVNbcg6XyiKRYQEYH5K7AhbzCwGgwH5ZXU4cr4YBaW1mMjlpVqdHmU1zSiraTbGgv08sXJ+HBanxsLZkZPqEhFNp9JKk86NbIRARERE0+DAgQN44oknAIg7KQ79OTw8HO7u7ggPD4enp6cx7uHhIdqXl5eX8ee2tjazY3V0dAAA2tvb0dnZCbVajfb2dmP8mmvXd1dUVOC73/0uHn30UTz11FMTun/WxGqLG235gnpbNNlVwmTNLP3dsuC1YvHvtQXbW/ISl1k4s6RguP46Ix7bPBQZGoiNNyzGF99mG2N6vR6vvPk+HnlgO2anjD7r51iYdm6sqq5Bd3c3XF1dx72vi3mFeP2dndDr9aL4rWtWIDwkYPjHZ4THPDE+Do88eC8+/OhTNLe0GuMGgwFfHvgadQ0N2LD2pkkb0JxeEv5uWnpsSzbn58+0Mhj0aG1rR2NTCxqamtHY1ILG5hY0NbdApxP/jspkMsjlMmPBo1wuh1w2dNnkNrkMcpl4WXZ1fW8vTyyYMwtBgf5Tfh+1Wi2+OPitKObk6Ih1N3Egvb2jCyfOXMTx0xfRZjKL62To69fgcNZ5HM46j4SYcKxcMg9zk+OgUFy/iH0oab+XWnBsvp9JQKrH3MLjWvLdUMrXmSXf5S39Pm3Jlw0Lj20rfyv39/ejqqZOFIuJDJvga8bS+2zJ321S/r0p4fdpIiIiIpo5pmIGbVsdU7WRv7cml40+VzS9pHyZWNn7ydYNq1BUVoW2jm5jLP9yMV5/Zycevm8rVCrLi0pMx0QtmfD1+Mkz+ODjz83i9265BW6uzjA792DyeO/e+zVa2tqhUqmQmJiIyspKQNBjwZxZxk6IJWXleO3Nd3D3XZsREDD1Yy6jiYmKwA8fuQ9/euVtUQfHvd8cw5yUBISFBE3+QWXjG28w21ymv/5KIxDkFow/67XXX2c0lhxbmPh9BgDILPi8lluwrcGyvA2CDG0d3WhsaUdDSzsaW9rR2NKB7p7ewTFQ2eB4p0wuGxwHNd4uGzIGKjMZDzXfh4ebM2bFRcDTffzXVAzJfExrCYKALw5nwzBkvEEhV+DWG9Ns73q7Sc63s6cPWbllOJ5Tgub27utvME61Te3Y8dVZfHrkAtJmRWPF/HiEBnhP+nFGJ+X3aAlfX5aMr8GSsSJL30ct+Oyy6HozC58ri/5es/A1arDkuZaQJZ/Xlh3Ywu1t52/z0grT4sZQC98bJGBrn9MzHZ+vcRru8eJjSES27fXXX8cLL7wAYPAasoyMDGRkZGDWrFlITU2Fu7v7lOdxrdBRrVbjxIkT2L9/v7HgURAEvPbaa1Cr1fjjH/845blMJVus/iAimvHWr1yI1o4unDibb4xpdTq88tYHuH/7ZixZONei/cfFRpvFikvKMHdO6rj2c/L0eby7aw8MJieeVi5dgHU3pE8oN38/Xzz60Hfw8Z69KCopFd129vxFNDQ2Ydvm2+Hu7jah/RPZA4PBYCxibGxuRkNj84hFjCMRBAF6vQC9fnJOBJ4+fxFzUpJw08p0eHpM3Zf5o5mn0drWLoqtvWE53NwsGUi0bRqNFp9/fRzfZp03ez8eiY+XJ/x8faBSKaFSqYzdPXv7+lBT14D+fs2o218pVeNKqRo+Xu7YftsazE7iLLFERFOpQl1jVogZzc6NRERERERERGTDXF2c8MP7N+HFNz5GT2+/MX4xrxB/ef1d/OC798LJydGiY0xGcaMgCPj6yAl88sVBs9u23LoWc1ISrruP/MslOJF93rgsl8tx46oVWLFkPr46dEQ0iWxLaxtef/tdbL7tFiQnXX/fUyk2KgKPPXQP/vqPd43jDwaDAe/s+hz/78ffG/fkh0TjJQgCWtu7rhYvtqOhpQ2NLR1obm2DVqub9ny+PZmLJXMTsHxhCpwcHabsOGfzSlBV1ySKZSxIgb+P5whb2L9+jQ6fH8vFsZxi6MY4vu3s5AQvTw+oVEoolQoY9AZodDr09PSirb1j1G37NTocO1+EY+eLkBQVhO3r0hDgPfUXtBIRzVStbR1oa+8UxWIiQyXKhoiIiGaCAwcO4Pnnnwcw0IHxsccew/bt26elmNGUu7s7UlJSkJKSgnXr1uG5555Dfn4+PvzwQ+zcuROCIGD//v3w9PTEL3/5y2nPb7KwuJGIyAbJZDLcfctK9PVrcPZSsTFuMBjw9gcfI7+wCFvv2Ag3V5cJ7d/Z2RkR4WGoVA/OeHSluGTMxY1d3d3Y+clenMnJNbtt8bxZ2HbbzRbNGOjk5IR7tt6Bb4+ewLHMU6Lb1FU12LF7D753/z3GWUyJ7JXBYEBbe8fV4sWhRYyt0Ommf8BuNIIg4EJeAfIKi7B00TwsX7rI4osOTLW1d+BE9llRLCIsBAvmzprU49iS4vIqvLv7ABqaW0dcx9XFGXExUYgMD0FEWAjCQ0PgPkoxqMFgQENTMyqralCprkGFuhqlFephu5u1tHXib+98giXzU7D1lpvg4uw0KfeLiIjESisqRcu+Pl5TOpkAEREREREREdF0CA30ww/vux0vv/0pNJrB7ndXSsrwP3/8G+7ffgfiYqImvP+EeHFxY3FJGfR6/ZiL8jo7u/Dh7i9wPjff7LaNq1dg9Yol191Hb28f3v3oC1HM0dEB3/vOXfD18UZIcBB2frwHXd2DHcg0Gi12fPwp7r9nK2JjzCetnU4piXFYe+NyHPjmqDGmrqnDgW9PYOOalRJmRvZEEAS0dXShodm0iLEd2uEmdpWoi5NOr8eJcwU4n1+KlYtnYVFq3KQX+fb1a/BN1gVRzMfTDSsXj2+iantSVNmAd/dno6lt5E6Njo4OiAwLRcTV8dCIsBD4+XiPeE1JZ1f3wFjo1X/llVVo7+gcdt3C8jr89h9fYtOqeVi1KBFydnkiIpp0pl0b5XI5IkKnoFM4EREREQC1Wo3HH38cALBu3Tr85je/kaSocTQpKSl47rnn8LOf/QyPP/44srKysGPHDqxfvx5Lly6VOr0JYXEjEZGNksvleHDzGigVCpy6cFl02+nzF3G5uBT3bLkNc1OTJ7T/+NgYs+LGsTifm48PP/4cnV3mJ47TF87BvZs3QC63/GSuXC7H6htWICgwAJ9+sR9a7eCAZlV1Lbq7e9i9keyGaRFjY1MLGpqarbKI8Xp0eh2OnzqDcxfzsDJ9MRYvmDNpg3o1tfWizoRyuRy33nyTRcXUtmqgW+MJfJt1FsPUHAIY6Oi1MiMNC+akQKVSjXnfcrkcQQH+CArwR9qCgU7BzS2tOH7yDE6cOie6wOKaU+fzUVhciXvvWMsujkREU6C0XC1ajo4IkygTIiIiIiIiIqLJFR0ejB/dvwl/e/cz9PVrjPHG5hb88W9vYtWyJdi0YQ0cJ9AlzbRzo0ajQaW6CtFRkaNuJwgCzl64hB27v0R3T4/Z7RtuWo5b1o6tsG/3l1+bdQjbfMs6+Pp4AwAiwsPw2PcewI6PP0V1Ta1ovTPnciQvbgSAjWtvwMVLhaitbzDG9n5zDHNSEhAWwovOaeyMRYyNLQPdGFvb0dDcPnIRo5Xq6evH/mPnkH3xCm5aOhcpceGTNl5ZWdOIvn6tKHbLDYugVM68Tqn9Gh32HL+II+eKRlwnPDQYKzPSsGje7HF9Tri7uWJWUjxmJcUDGBivv1xciqOZp3Exr9Bs0letTo+PvjmL85crcd8t6eziSEQ0ycoqxcWNEaFB47rOhYiIiGg8rnVsfPTRR/H0009LnM3oPDw88Oabb+K1117DCy+8gF/84hc4cOCA1GlNCIsbiYhsmEIhx32bboKTowOOZIu7JHZ0duGVtz7A4vlzsPnWm+Hl6TGufSfEx+Kbw4MzbBYVl466flt7B3Z/fmDYbo0AcNOyxbhz4+pJKWwcalZyIto7OnDwmyPGmEqlmvSOcETT4VoR40DxYhMam1rQ2NyCxqaWSS1idHVxhr+fD/z9fBHg5wt/Px9jdyeDwXD1nzDkZwMMgsny0NtHua2ruwdncnLR198vyqGntxf7Dx3FqXMXsGZVBlIS4y0e1PMwKWgWBAE+3p4W7dMWqWvq8caOL4ft1qhUKrFk4VyszEhDeGjwpB3T18cbmzauxcabb0TOxXwcPnESZSYz57V3duFv73yCpQtm4Z7b10Kl4p8iRESTpbRCXNwYExkuUSZERERERERERJMvLjIEj3/3TvzlnT3o6u41xgVBwOHjJ5FXcAX3bbsD8bFR49qvn58vPD090D6kuPBKUcmoxY0dnV3YMUK3RgDYcssarF45ttnRK6tqcSL7vCiWGB+DFemLRDEPD3d89/578Oe/vy4qhHR2dh7TcaaaSqnEA3dvxu//9JpxEkqDwYBdnx/Ek99/QOLsyBpdK2JsbGlHQ3PbQCFjSzuaWtqh1ekw4syd4+Ti7Ag/b0/4+3giwNcT3h4DY4nXxjcFQRgY1xSujW8KMBj0gz8PjQtDxkCvbXd1XLS3X4PCEjUMJnm3tHfhowMnEJrji5uXzUdEiP+k3KehZDIZIoIt36+tKattwVtfDt+tUalQYMG8VKzKSENURNikFJbK5XIkJ8QhOSEOLW3tOHHyDE6cOouOzi7ReiVVjfjtP77EnTctwMoFCRYfl4iIBph2boyJ5ESvRERDyWSyGdkAwhQfApoMnZ2dOHDgAJYtW2b1hY1DPfroo2hra8Mbb7yBr776CmvXrpU6pXHjFcVERDZOLpdh64bliAwNwK59x9HbJy4gOn3+Is5euIR5qclYmZGG+NioMX2JTYiLES1fGaa4URAEXCkuw9HMbFzIKxR1TLvGxdkJW29bi7R5s6bsy3OZSaeauJgozs5EVk0QBLS2tZsVMTY1t4q6kFrKxcXZWLx47X9/Xx+4urpM2jHGIiNtAQ6fOIUzOblm7xOtbe3YtWcfQoPP4eYbVyAyPHTCx/H38xEtC4KApuZWBAXOnAG9ojI1/v7up6LZq6+JjgzHA9s3IzDAb8qOr1IqsXjBHCyaPxtZp8/jo8/2oc/kc+nkuTy0tHXgB/dthtMEZtImIiJzpt+Ho6NY3EhERERERERE9iUiJAA/e3Qr/vnxVyhVizsYDnRxfAPxMVFYmZGGualJUCqvfzmMTCZDQlwsTp8dLDC8UlyCdWtvMlu3sqoGRzOzcfp87rBjOa4uzrhn80YsmJM85vt0OPO0aNnR0QH3bd007JhqX18f2js6RbHE+LgxH2uqRYaHYu2Ny3HgmyGT55ZWoLq2HqHBgRJmRlK6bhHjJHF2dIC/r5exiNHf2w3+3p5wdXEa/zUKEyysbGrtwDdZF1BYWmV2W3V9M97c/TUSo0OxJmMe/LzHNzH1UP4+4oldBUFAU2sHggKdJrxPW5NXWofXPjs5bDfP2KgI3L99MwL8fafs+D5enrht/WqsvXE5Pv3yKxzNzBbdrtXpsePgabR0dGPTqnm8yJyIaBKUVlaLlqMjJn5tEREREdFoduzYAZlMhl/96ldSpzJuzzzzDA4ePIgvv/ySxY1ERCQNmUyGJXMTkRgdhg++OIxLVypEtxsMBpy7mIdzF/MQFOCPFRmLMTc1Gd6eHiOeSE2IixUtXykuATBYlHXhUiGOZmWjvqFpxLxmJ8fh3js2wNPDbcR1LNXX14fScvH9TUniDHhkHQaLGJuvFjIO/N/U3DJlRYz+vr4I8PcdoYhxcmY5HS9XVxdsXHsDliyci6+PZKLgSrHZOtW19Xjz/Y+QFB+LNasy4OfrM8yeRufo6AhPD3fR4H5jc8uMKW7Mu1KG197/zGwwWKVU4rb1q3HTynTI5fJpyUUmkyEjbQGSEmLx/q49yL8sfs6vlKrx8hs78eOH7oKL88wZbCUimirs3EhEREREREREM4G/jxeefHgLvs3KweffZJkVthSVlqOotBwe7m5YtmQhli9dBG8vzxH2NsC0uLGoZHDCV61Wi7MX8nD0xCmUq6uH2xwAMD81CXdv3gB3N9cx35eu7h6czckTxW6+cQV8fbyHXb/gchGEIUVXjg4OiI2JGvPxpsPGtTcgK/ucqJPZ0ZNncc/mjRJmRdNhaBFjY3MbGprb0NDcMqVFjP4+HgjwGfjZrIhRMC96m2p+3h7YvnEFKmsacfDEeVTXN5utc7msGkXlNVgwKw6rFs+Cm+v4u686Oqjg6eaC9q4eY6yxtQNBgQEW5W8rzhZW4a292TAYxOPeKqUSt29cgxuXL5228VAnR0fcfeetmD8nBe/u+BTNrW2i2786mY/ePg22r0uDnAWOREQWKTPr3MjiRiIiIpoamZmZWLduHcLCbLNT9LZt2/D6669LncaEsLiRiMiOeHm44gf3bET2xSvDdnEEgLqGRuz6dC92fboXbq6uiAgLRkRYCCLCQhAcGACVgwoOKiVCQ0NE2zU3t+D/Xvwbmlvb0dXdY7bfoaajW+M1V4pLodcPDk4oFAqzrpNE0621rR1fHjiECnX18AN2E5zx08XFGf6+PleLFwc7Mk53J8aJ8vXxxvbNt6CyqgZfHT4OdXWt2TqFRSW4UlKGBXNm4YZlS+A2jgsBACDAz1dU3NjQaD5waI+ulFbi1ff3QGdyIUdURBgevPvOKe3WOBofL0/86JH7cfL0eezasw99/YOfS+VVdfjL2x/jJ9/dyg6OREQWaG1rR3GZeLKPGHZuJCIiIiIiIiI7JZfLsXrZAqQmRuOdT75CmbrObJ2Ozi7s+/oI9n19BP5+PogMC0X4tTHR0BA4D5l0LyFePOHrqTPn8cHHn0NdVYOq2nroRinMcnVxxvY71mPhnJRxj4lmnbkgGkNSKBRYvmThiOsXFF4RLSfEx46pO+V0UimVWLZkIfZ9fcQYyz6Xizs2rIazk6OEmdFUMBgMOH7mEgpLKtHU2gGN6cSugmHC+3Z2dICfjycCfL1GL2K0QhEh/nj4rrUoKFHjm6wLaGnvEt1uEAScuVSEi5fLkDE/GenzEuHgoBrXMfx9PUXFjQ3N7ZOSu7W7UFyDt77MhsFkrD0mKhwPbL9zSrs1jiYxLgb/+bMfYc+XX+GISRfH4znFkMtl2LZ2sdW/domIrJVOp0NBUakoFhNhm8UGREREZP3UajUee+wxqdOYsGXLluEPf/iD1GlMiHWd6SQiIosN7eK498hpnL54BRrt8INuXd3dyL9cbNZRCxgYjJDL5TAYBgcdTp05j6CgoBGP7eCgQtrcZNyyZtWUdmscKt9kIC82OhKOjhwcI+kYDAbs2P0F6hoaJ7wPZ2cnYyfGAD+/qx0ZBzox2sOgR0RYCL73na0ouFyMr49koqWtTXS7wWDAmZxcXMwrxLIlC5G+eD4cHMZW/Bbg54ui0nLjcmNzyyRmbp0qqurw93c/NStsnJuajO/dtxUqiS9ukMlkSE9bgLDQYPzp1X+iq7vbeFuZuhavvPcpfnj/nVCp+KcJEdF4GQwG3P+Dp9FtMvlIQmy0RBkREREREREREU2PQD9vPPXwXTh8KhcHjmajq7t32PUam1rQ2NSCMzm5xpizkxNUKiWUSiVyc/NF6xcUXsGxrNOjHlsul2PR3BTcecsaeLiPf0zUYBBwLOusKLZgziy4j7Cvnp4elFeqRbHkxIRxH3c6LF+6CAcOHTOOMfdrNMg+n4tV6Yskzowm25FTF3Ek+4JF+3B2dBxSxOgJ/6s/uzqpbHpMVCaTISUuAonRoThzqRhHT+ehx2Riao1Wh8PZuTibV4wb0mZjXnI0xtpw0N/HE8UVg5PINrbYf3FjYUUD3vj8lFlhY9qCubh/+x1QKBQSZTbAydER2++8FRHhoXh356eiTrtHzxXB2dEBt6+aJ12CREQ27D9++zKqaupFsdgoFjcSERHR1FCr1TbbtREAwsPDIQgCqqqqbO5+8ApiIiI75eXhintvuxF3rMnAqQuFOHb6Euqb28a8vVwuh4eHB9qGFB21t7cPW9wY6O+DlWlzsWT+LDg7OUKmmJ4OXBqNBsVDipgAICXJOgfyaOa4mFc45sLGoUWM/r6+Vzsy2k8R42hkMhlSkuKRGB+DszmXcPjEKfT0ii880Gi1+Pb4SZzJycWNy5di3uwUyK8zqufv5yNatvfOjd09vXjlvU/RrxHPhpu2cC7u3yb9QN5Q4aHBePpHD+OlV95CW3uHMX65pBK79x/B9ttWS5gdEZFt+u8//BV7vzosit1843KEhwZLkxARERERERER0TSSy+W4KWMBVqTNwYX8YhzNvoCSiprrbtfb14fevoGfHUwmTe3s7IRerx/2/LqXhzuWp83DssXzBiZ6lU/sHHxhUSmaWlpFsZUZi0de/3KRaEJalUqJOCud3MrbyxOzUxJx4VKBMXY06wxWLl1o92NfM0lHZzdOnL005vWvFTH6DylkDPD1GrkTo0FvHrNBCoUCS+YmYm5SNE6cK8DJnMvQ6cX3rbO7F59/m41TFy5jTcYcxEUGX/d3JcDHU7Rs78WNLR09eG1PFnR6cTfQlRlp2HbHxuuOH0+n9MXz4eTkiDfe3QX9kOf6QFYegv08sXiWdb53ExFZq91ffo3n//qWKBYfE4kFs5OlSYiIiIjsnoeHh9QpWMTd3V3qFCaMxY1ERHbOxdkRNy6dixuWzMHlsiocP5OHwhI1evs1193W09NTVNw49GdnJ0ckxUViZdpcxEeHSzIYVVRSBp1usCulXC5HYnzstOdBdI1Wq8Who5lmcWcnp6tdGH2NXRgD/HxnRBHj9SgUCqQtnIs5s5Jw4tRZZJ0+D51e3G22s6sbn+3/Blmnz2PtDcsRHxs14uMW4OcrWm5tb4dWq4VKpZqy+yClXV9+i/bOblFs/pxZeGD7ZqsayLsmMMAPP3nsQfzxr/9A15AuY0dOnsf8WfFIiImQMDsiItty4NBR/PJ/XxLFAvx98Y+X/1eijIiIiIiIiIiIpKFSKrFoThIWzUlCdV0jjmZfxOkLBWYTAw7H09PTLNbe3g4fn4HJFGUyICEmCqvSF2B2UjwUCsvPvecWFomWQ4MDERM18vnxgsvi9eNjY+DgMD2TzU7Eqow0UXFjbX0jmlvb4OfjLWFWNJm+PZljVqQHAE6ODleLF73g7+1u7Mbo5uo8o8dEnRwdsDp9LhalxuHbU7m4eLlc1NkPABpa2vH+F0cRFRaAtRnzEBLgM8LegABf8ftWa0c3tFodVCr7uwxQEAS8d+As+jTi8eOMtAXYvvkWq3xdzZ+dgu/euwX/eHeX6Hne+dUZJEQGwdPNWcLsiIhsx5WScnz38f8SxVQqJf755/+2yuthiIgkJZMN/Jvp+BDQJAgLC0N+fj7S09OlTmVC1Go1ZDKZzXVtBFjcSEQ0Y8hkMiTFhCMpZqDdcGNLO9S1jaisaURlbQPUNY1mBY9eXl6oqKgwLjup5Pje9lsRERIIPx9PyU8U5xdeES1HR0bA2Zkngkk62WcvoKOzSxTbvvlWJCXEin9fTAarCHBycsTqVRlYPH82vj1+EjmXCswG9RqbW/D+x58hKjwMN9+4HCHBgWb78fMVD4wLgoCm5lYEBwVMaf5SuFhQjNMXCkSxxLhoPHTvFqs+kRsc6I8fP/oAXvjLP6DVDl5Y8s7uA/jPnzwIRwf7LEQlIppMFepqfOexp0SflXK5HB+89iJCQ4IAwTDK1kRERERERERE9is0yB/33L4aWzfegJr6JlTW1KOypgGVNfWoqW+C3qTzl4ODA1xcXNDTMzghn4+nGzatuwERoUGICA2Cq8vkjj9WVtWKlhfMTR1x3LWvrw+l5RWiWHJiwqTmM9kS4qLh6uKM7p5eY6yyqpbFjXaivqkVOQUlotjcpFisWbZAVMQoGHTDbT6jebq74o41S7F0biK+ysxBqbrObJ3yqga8tvMgUhMisXrpHHh5uJqt4+cl7sIgCAKaWjsQPEpBpK06cbEMhRUNoti82Sm4967bJb9eZTQL5qait68f7+3aY4z19Gnwwf5sfH/LSl53TkR0Hd3dPbjr4afQ2SWe7PvFX/8rliyYYzddnomIiMj6hIeHIzMzEw8//LDUqUzIgQMHbLb7JIsbCQCs+oTPaEyLHmg6WPJasfD5kllSqGHJxb0SFohYelHyCL/bMpkMAX7eCPDzxsLZA4NfgiBApzdAp9NBo9VBpnTAG+/64KkLF4zbyWUwrn81wZEStyzvMdBqtSgqKRPFUpISOPOGFPheDADo6enFsazTolh0ZLh5YSONysPDHZs2rsXSRfPx1eHjKC6rMFunXF2FV//5IWYnJ2L1qgx4eQ5+EXdwcIC3lyda29qNsYamZrsrbuzu6cUHe74WxZydnPDgPVugUlr/V/yIsBBs2rgGH+3ZZ4w1t7Zjz8Gj2Hbragv2bOH7kUXvZ1Iee4ay5L3Vksfb4qIxS45t6evMgtwtebzlFr4vyRSWbS+Zqfm97u/vx7bv/gQtrW2i+G//62ncuGLp1eNK+Z5iybH5nYmIiIiIaMaQ6pwpz8GQ1bP0d0PCv8ulOl83wnGVKiUiwoIQERZkjGl1OjQ0taK3TwOdTgetTg+lygGnso7jXE6ucb3k2HCsu2Hp0AQnnp8Jvd6Aqtp6USwyPGTE9S9fKYZ+SIc8hUKBhPjYSctnKsjlcoSHhaDwymABXGVNLRbMTbFwzxa+RuUWnGc0WHBsSz/zLNne0nOrMvPX/tcnzkMQZLj2fDiolFi7Ig1uruIiYNkw246VILP0PLwF13gIU18sEeTvjfs33YiSylp8lZmD+qa2q7cMPteXrlSioLgKaXMTsGJhCpydHI23OTg4wtvDHa0dg5PuNrS0IzjAd+JJWfA6m6qx8Ob2buw+nCuKeXl64P5td1j1RK/XLFuyEJeLSnFmyOdLbnEVTueXI21W9MR3bNG1U5aS8Lu0RYe24D3F0rtswXuhRc+1xU+VjY5n2urfe5Z8Xtvy0NoIz7UgCPj+z36FS4XFovj9d92KHzxw19XtbPS5lnIY1aJj2/ILjYiIaHw2bNiAJ598EtXV1QgNDZU6nXF79dVXkZGRIXUaE2L9f+kTEdG0kMlkUCkVcHZyhKe7KzzcXOHj5Slap3vIjKVSKykrh0Yz2GlSJpMhKSFOwoxopjuWlY2+/n5RbO0Ny1nYOEGBAX64b9sdeGD7ZgQF+A+7Tm7BZfzptX/iUoG4i2uAn3jgrrGpZcrylMquL79Fh8kMdXdt2iAq9LR2NyxbgtjoCFHsyMkcXClVS5QREZFtePw/foPT5y+KYptvWYtnfvKoRBkREREREREREdkGlVKJ0CB/xEWFIikuErOTYpCSEIPgQPE4xNCOg5OtrqEJWq24o11E2MjFjQWXi0TLcTHRcHR0HGFt62F6nyqrzDvUke0pU9eiqLxaFMtYmGpW2EhjExsRjMe2rcMda5bCw83F7Ha9wYCs84X40ztfoLhC3PHV31d8LUdjSzvsiSAIeP/gOfSbvF9+Z+smODs7SZTV+G3bvBHubuLum7u+OoP2rqn7nCEisnV/eXMH3v9knyg2Ozkef/u//+Q1WERERDTl1q9fD0EQ8Pjjj0udyrg9/vjj6OzsxPbt26VOZUJY3EhERCNycxWfQO/qtp7ixvxC8UBeZEQYXF3NT/gTTYe29g5kn7sgiqUmJyAkOFCijOxHTFQEvv/QPdh8y83w9HA3u12v12PvV4dhMAzO6Obv6yNap6GpecrznE5l6lqcvlAgiqUmJ2DponnSJDRBcrkc92/fDJVKJYp/tPdbducmIhrB2x/sxqtvfSiKxcdG4Y0//x8H84iIiIiIiIiIJsjVRTzG2D2FY6KV1eICJR8vT7i5ug67rkajQXFpqSiWnBg/ZblNpohQcXGjurqW5/5tnCAI+Or4GVHMzdUZ6fMt7cg5s8nlcsxNisaPv3MLVqfPhaODymyd3n4NPvkqSzwe6i2e8LSh2b6KG3OKalBY0SCKpactwKwk23gPvMbN1RV3b7lNFOvp0+CzIznSJEREZOWyzlzA0798XhTzcHfDR68/DxcXTqZARERE02Pbtm24dOkSnnzySalTGbNf/OIXOHDgAMLDw5Geni51OhPC4kYiIhqRabFgd7d1zB6n0+lwuahEFEtJTJAoGyLg0NFM6PWDg0kKhRyrVy2TMCP7IpPJMDc1GT959AGsvWE5nExmJO7p7UVvb59xOcDfFzqdzjhIbm/FjUdP5YiWnZ2ccO9dt9tkUUuAny82bVwjilXXNaKkonqELYiIZq6c3Hz8y8+eFcWcnZ3w0Vt/HnYCACIiIiIiIiIiGpvpnPDVtLgxfJSujUXFpdDp9MZluVyOxIS4KcttMkWGi+9Xd08vmlvbpEmGJkXelXLU1IvH3G5YMg+Ojg4SZWRfVColli9MwU/vvw1pcxIgNxn36+nrR+eQ6zUCfL1gMBiM46GNzW3Tme6UO3KuWLTs5emBu25bL1E2lpk/OwWL5s0Wxc7kl6Ort1+ijIiIrFNDUwu2PfaMWZfzt1/+NeJjIiXKiojIRshkgEzOfzZ4/SRZp2eeeQbu7u7Yv38/7rrrLlRXW+/1rAUFBdiyZQt27twJmUyGl156SeqUJozFjURENCLzgbxuiTIRKyuvRH+/+ESvrcxSSvantq4BF/MKRbHF8+fC28tToozsl1KpxLIlC/HjR+43u61fozH+nJt/GafP56K6th7AQGdNzZDbbVlXdw/O5V4WxW6+aTm8PD1G2ML63bBsCfz9xN02j2VfGGFtIqKZqbWtHXc99GP09Ym/A7/6x99gdkqiRFkREREREREREdkHs86NPVNX3Nja1iFaDg0OHHHd/ELxeEBMVCScnW2jY42Pt5dZ0Vtbe6dE2ZCl9Ho9vsk8K4r5entg/izbKLa1JS7OjtiwciF+9J1bzG7r12iNPzc0t+F8finqmtoAAG2d3dAMud2W1TR1oKiqSRS745a1cHZ2kigjy921aQOUCoVxWac34OTFklG2ICKaWXQ6He75wb+iulbctfdff/w9bFp/o0RZERER0Uzl7u6Ol156CYIgIC8vD2vWrMEvf/lLVFVVSZ0aAKCrqwsHDx7Eww8/jDvvvBP5+fkQBAGPPPIIkpOTpU5vwpRSJ0BERNbL1UU8ONbd0wtBECTvDpZ/+YpoOTwsBO7ubhJlQzOZIAj46vAxUczRwQErM9IkymhmcHV1gVwuh8Ew2C2zv1+DlpY2/O7lV3HybA4AoKxCDV9vLzg7O6Gruwc+DrY/c2zWuUvQ6QdnaVYqFMhIWyhhRpaTy+VYmb4YH39+wBg7n3cFW7pugIebq4SZERFZB4PBgId+9P9QWq4WxX/48H34ztZNEmVFRERERERERGQ/3EzORU/lhK86nbgTzUhd77RaLYpKSkWx5KSEKctrsslkMjg6OKC/f3DySdMuPGQ7zuReRmt7lyi2ZtlCKIYUa9Hk8vFyh4NKCc2Q35t+jRYajQZ//WAfPjuUDUEQUFJZCx9PNzg6OqK9qwf+PrY/Ae+xHHHRn7ubK+bPmSVRNpPDw90N8+fOwulzF42xY+eLcFNaslmXTiKimei//u+v+PbEaVHspuVp+PW//lCijIiIiGimy8jIwIsvvognnngCMpkMO3bswI4dOxAeHo709HSkpqYiNTUV4eHhcHOb2hqGqqoq5OfnQ61WY+/evcjPzzfeJggCAGD79u14+umnpzSPqcbiRiIiGpGbq3ggT6/Xo79fAycnR4kyGsih8EqxKJaSaDsDeWRfSsoqzAoNli9dBBcX25g111ZdGxDv7eszxvZ/cwS7vziIzq7BCw70egMuF5dizapldtFJ02Aw4Fj2RVFswdxUuNtBAeDSxfPx2b5voL16UYdeb0DmmUtYf8MSiTMjIpLe/774Cj7ff0gUW7poHv7wm3+XKCMiIiIiIiIiIvtiNuFrd++UHUtrUtzooFINu15JabmoC5tMJkNSgm11yVOZ3DfT+062oa9fgyOnLohi4cH+SIqNkCijmcPRQSUqbjyfX4YP9x5FbWOLMabT63GlvBpL5yXDx9P2J4Tu02iRnV8pimUsWQiV0vYvcVyZkSYqbmxq60JBaQ1mxYZKmBURkfT27P8W//fnN0Sx0OAAvP+3/4XSDt7/iYiIyHatX78eH3/8MR566CF0dnYCACorK6FWq7Fz507Ruh4eHggLC4OXl5cx5u7uLlrH03PwOub29nbRbdf239bWZvxZrRZfnw4MFjPKZDLjz8899xy2bds2kbtoVfjNj4iIRmQ6kAcA3T09khY3VlRWobe3TxRLToyXKBuayQwGA74+fEIU83B3w9LF8yXKaGZxdBwobuzp6cGJU+eg0+ng7Gz+nhUU4I/td9wiecfZyVBQXIHmVvEfNCszFkuUzeRydXHBovmzkXX6vDF2/PQF3LwyDXK57T93REQT9fXhE3j2f14Uxfx8vbHzjZfhYAcdiYmIiIiIiIiIrIHphK9T2bnx2kVHg4Y/B55feFm0HBURDldX25rs0LQbmcFgkCgTssSJM7no6e0XxdYuX2QXY2/WztFBhc7uXuh0OmTnFmPf0bNwdjI/L6xSKnHXhhV20UnzdL4afZrBgk6ZTIYVSxdJmNHkiYkMR1hIEKpq6oyxo+eKWNxIRDNaUWkFHvzpf4liKpUSu157HgF+PhJlRURERDRo1qxZOHToEH7/+9+LChpNz/G1t7ejvb3d4vMl5ucOxa4VNQqCgJSUFPzmN79BSkqKRce0FnKpEyAiIuvl5upiFuvq7pEgk0H5hVdEyyHBQfCyg45sZHty8wpR19Aoit24It1sFlqaGo4ODiguKcene79GdW09DAbxF3onRwd8/6F78Nfnn4O/v69EWU6u/KIy0XJYSBCiI8MlymbyrcxIEy23tneirrFZomyIiKSnrq7FvY89KbroSy6X44PXXkRYaLCEmRERERERERER2RdXV5POjT1T17nRtPuMTqc1W0en0+HylWJRLDkpYcpymioarfi+jdSlkqxXR2c3TuYUiGJJsRGICA2UKKOZxUGlQlVdIz795hSulFdDr9eLbpfL5Lj1hjS8+dvHEWUnz8ml0lrR8uyURPh4e0mTzCSTyWRm46GF5bXQmTyvREQzRXdPL+565Gfo6OwSxf/wq59h6cI5EmVFREREZM7d3R3PPfccTp8+jYcffljUkVEmk4n+XXOtAHG8/0z3N9x+U1JS8Oabb2L37t12U9gIsHMjERGNwmW4zo0SFjcaDAYUXCkSxVKS2LWRpp9Op8OhY1miWICfL+amJkuU0czT0tqOssoqaDQDA+NDB31SEuPwH0/+i90VflRW14uW581OsatZcSPDQ+Ht5YnWtsHulJXV9QgJ9JMwKyIiafT392PrQz9GU3OrKP7r/3gSq1dlSJQVEREREREREZF9cnURT/g6lZ0bVSbFjaYFgABQVl6Bfo1GFEtOtL3iRq1WJ1o2Lewk63f4VI7oeZTLZVizbKGEGc0s3b29yCkoR1d3HyAD9EMmwgsJ8MEzD9+JeckxEmY4+Srr20TLC+bOkiaRKbJg7iy8/9FnxmWd3oCaxnZEBLE7GRHNLIIg4F/+32+QWyC+DvDezRvww4e2S5QVEZHtMi1+mqn4GNBUc3d3xzPPPINnnnkG+/fvx759+5CVlYWOjg6zdS15PQ7XvTEjIwPr16/Hhg0bRMWV9oRnDomIaEQKhQJOTo7o6+s3xqZyptLrUVfVmBVXJiewuJGm36kzOWjv6BTF1tywDHI5m2JPF0EwICw0CI5ODmhubkN3Tw+CAvzw3e9sxV23r7e758JgMKCqtkEUiwgLkSibqRMRFiIubqypx9IF9jVoSUQ0Fk/9/H+Qfe6iKHb7+tX418cfkygjIiIiIiIiIiL75ebqKlqeysle3VzFhZSNTS1m6+QXXhEtR4SFwt3dbcpymgpd3T3o6RWPK7u5mk+sS9aroakV5/PEHUQXzEqAn4+nRBnNPF09/QgN9IGDgxLtnd3o6umDt4c7brtpMX54zwY4ODhIneKkauvqRUd3nygWGR4qUTZTw8XZGf5+PqL3fnVdC4sbiWjG+dtbO/Dux1+KYqlJcXjl+WdZmEJEREQ2Yf369Vi/fj0AoLOzE5mZmcjNzUV+fj7a2tpQVVU1bNHjaDw8PODp6Ynw8HCEhYUhNTUVqampdtWdcTQsbiQiolG5ubqIihu7JOzcaDqQFxjgD19fnuSl6dXb24djWadFsaiIMMTHRkuU0cy0elUGdHo94vV6HM3MhpOjI371b08gbeFcqVObEvVNrdCYzHBsr8WNFy4VGJfVNfWjrE1EZJ/e3bkHf3vjPVEsNjoCb/31d3ZXvE9EREREREREZA1cXcRFd1M52WtYSBCAC8blyqoa0e16vR6Fl8UdbJKTbK9ro+n9UimVCPT3kygbmoivT5wRdQpwUClxw9J50iU0A61eOgd6vR4qlRL7jpxBR1cv/vWxLbh52XypU5sSlXWtomUnR0f42+H1IBFhIaLixsq6ZixDnIQZERFNr5NnL+DJZ38ninm4u+Gj118w+15OREREZAvc3d2xbt06rFu3btjbOzsHmum0tbUNe7uXl5fddmMcD6sobmQrXJooS183w7VsJXtlwWvF4rcnS3Zg6cEt2P7q74eriwuamgdPInd39xhvu962Fhx8mF0KKLgiLm5MSYofZl1b/Tyx5fcjC3K3wffhY1nZ6OsTzxi5dtWygVfetNwf6R4za/rcXDg3FcdPnkV3Tw+WLpwHJydHXCq8gsUL5tjl98rK6jrRspenBzxsbJbmsTAt2KyqbYDBoB97Mc8UfP6MfVNbPbb1/F6Pm2DJ9ztL3icsfMykfC+VSVQYZ/F9Nkx8Uxv7TLiYV4jvP/VzUczZ2Qkfv/VneHm4T8/rx9JjSPWYS5q3pc+Lbb1OZzxLXmt8qomIiMjqSPkFRaK/j23s78RJY0XndomsjiCYdVPs6u6est+biNAg0XJtfSP6+zVwdBzowFZRqUavyThUcqItFjdWi5bDQgKhUFh4flLK9zJbPa87wc+9cnUtrpSpRV8V0hemws3NZeSNJunYA9tK+FqRKyw4rgXnsocxf1Ysjp/LR09vP9LnJ8FRpURNQysEWH69lBmLHvPJGXuorG8TLYeHBtvlhH8RoSE4m3PJuFxZZ97Bd1ST/DobF6nGmQDY7HUpFj1fFvyeW/JeBkj7OrOEJe+Nlr5OLHnMLPndsrG/9RqbWrDt0Z9BazK595sv/hIJMeFjfxwteo1aOqZo4e8XERERzTjXChdZwDg6+zsDQEREk2rYwTwJVNfUoqOjSxRLscFZSsm2tbV34NTZHFEsNTkBoSFBw29AU8bBwQGrMtIAAC4uzpDL5aisqkFRSbm0iU0RdU2DaNkeuzYC5vdLo9Whvql1hLWJiOxLW3sH7nrox+jtFV+89vcXnsOcWUkSZUVEREREREREZP9cXcTjob29fdDr9VNyrPCQIFFRkiAIqKodnOAwv1A82WtoSDC8vDynJJepVFlVK1qOCAuWKBMaL0EQ8NXx06KYq7MzMhbMkiijmcvJ0QHLFw487q7OTlAqlaiorkdJZe11trRN6nrxmOBMGQ+taWyDboo+c4iIrIler8e9P/w3VNXUi+LP/PBBbN5wk0RZEREREZG1YHEjERGNytXFWbTc3dMrSR4Fl4tFy36+PvD385UkF5q5Dh3NhF4/OPuXQqHATSszJMxoZls4LxVenh6i2NdHTsBgsNFZBEfR3iku7g4O9Jcok6nl7uYKN1dXUazdpLCdiMgeGQwGfPdH/4ri0gpR/PsP3YP7t90hTVJERERERERERDOE6WSvANAzRWOiDg4qBAX4iWKVVTUABorKCkyKG5MT46ckj6l27T5dExHK4kZbkV9Uhuq6RlHshqXzjN1FaXotnh0PT3fx2Nk3WRch2FinrrFo7RS/74YEB0qUydQyvV86vQFdPf0SZUNENH2e/d1f8c2xU6LYjcsW4b//7UcSZURERERE1oTFjURENCrzzo09kuTR3CKepc/D3U2SPGjmqq1rQG7+ZVFs0bzZ8PH2kiYhGiguXZEuijU0NeNSwZURtrBdWp1OtGzPA8iODirRslbHmUqJyP797uXXsGff16JY2oI5ePG//1OijIiIiIiIiIiIZg7XYYobu3umbkw0IjRItJxfWDRwzO5us+P6+4kLIW1BbX0jWlrbRLFwdm60CXq9Hl8fPyOK+Xp5YkGqbRbZ2gOlUoFVaamiWF1jC/KKKiXKaOqYjgk62el46HD3S8PxUCKyc58dOIz/efl1USwkyB/v//V/oFQqJcqKiMiOyGSAnP8gk0n9TBCRBfitkIiIRuXqIh7Mm8qBvNFERYSh8Mpg98bS8krU1TcgKDBAknxo5vn68HHRDJiOjg5YmZEmYUYEAKnJCThx6izqG5uMsUPHspCSGGdXJ0C1WvGAlkqlGmFN22d630wLO4mI7M2ho1n4+X//QRTz9fHCzjdetutidiIiIiIiIiIiazFc58apnPA1ITYKp87lGpfzLxejqbkFvj7e8PL0QFt7h/G27DPnkJgQN2W5TIVjWdmiZTdXFwQH+EuUDY3HmYuFaG3vFMVWL1sAhUIhUUYEAHOTopF1vhCNLe3G2LcnLyI5NsyunhutziBattfx0OHGsHUsbiQiO1ZcVokHf/pzUUypVGLnK79DoL+vRFkRERERTb2uri60tbWhs7MTbW1tw67j5eUFAHB3d0dYWNj0JWeF7OeKbyIimhKuLs6iZak6Ny6YNxtHM0+hp6fXGDuelY277rhVknxoZikpq0BJuXj2y+VLFw07ky9NL7lcjtUrM/D+x58ZY23tHTibcwlLFs2TLrFJJ1x/FTs1tKiYiMjeVFXX4p5Hn4DBMHjRhkwmw/uv/hERYSESZkZERERERERENHM4OjpCLpeLztF0T+GY6II5yfjo84Po7esHMHAe/FjWGWy+9WZkLE3D3gNfG9ctKStHdU0tQkNso/NhX38/Tp7JEcWWpc2HQiGXJiEas/5+DY6cyhHFwoL8kRwXKU1CZCSXy3Hj0jnYufeYMdbS3onz+aVYNNt+umoKJuOh9txzRSaTicZADQaOhxKRferp6cVdjzyN9g7x5AnP/+IpZCyeK1FWRERERJOnq6sLubm5yM/PN/6vVqst2qeHhwdSU1ORkpKC2bNnIyUlZUYUPrK4kYiIRmVavNU9pLhwOjk4OGDJovn49mimMZZXcAU3rmiBr6+PJDnRzJGTmy9adndzw9JF8yXKhkzFx0YhIiwElVU1xtjRrNOYNzsZjo6OEmY2eUxn8NRq7beboWmnRgcV/2QhIvuk0Wiw7Xs/RWNTiyj+3L8/gbU3LJMoKyIiIiIiIiKimUcmk8HV1QWdnV3G2FRO+Oro4ID0RfNw6PgpYywz+xxuXXcj5s+djaPHs9DV3W287diJk7h76+Ypy2cynTmfi76rRZsAIJMBy5cukDAjGqv8onL09PaJYjevWAyZzJ5LzGxHUkwYQgJ9UVPfbIwdyb6EuUnRUNnJWJrKpAhaq7fPboYGg8FscleVyn46cBIRXSMIAn74b/+Ni/lXRPF7Nm/ATx6+F4Bh+A2JiIiIrFxVVRV27NiBAwcOmBUyTkYzj/b2dmRmZiIzc7BmIjw8HOvWrcPGjRuRnJxs8TGsEadGIyKiUZkWN0rVuREA0hbOg6Ojg3FZEAQcP3lasnxo5mhr7xAtL100DyqVSqJsyJRMJsOaVeIikO6eHrOZgW2Z6aCkRqORKJOpp9VqRcsqpX0MyBIRmXr6v/7X7LPq1nU34t+f/IE0CRERERERERERzWBuZhO+Tu2Y6IqlC82Od+5iHlQqFdKXLBLdVnilCA0NjVOaz2QQBAFHTpwSxVKT4uHr7SVNQjQure3ijkrR4SGICA2UKBsyJZPJsCZD3OGqq6cXpy5eGWEL26NSigv8tBrtCGvaNs0w98tByeJGIrI/r/xzF/6563NRLCUhBq/8/llOnkBEREQ2KSsrC1u2bMHatWvx+uuvo7KyEoIgGP8BA3+/T8Y/AKJ9V1ZW4vXXX8edd96Jm2++Gbt27ZLyoZgSLG4kIqJRmQ7k9UhY3Ojs7IzFC8Qn7C/k5qPdpPCMaLKZDjB4eLhJlAmNJCIsBAmx0aJYZvY59EjUbXayebiJ34vrG5okymRqdff0oGPIzNgA4G5y34mI7MF7u/bgL6+/I4rFRIXj7b/+HnI5T9UQEREREREREU03V5fpLW4MDPBDUrx4XGPvwcPo79dg0YJ5cHZyEt12PEtcNGiNzuTkorq2XhRbmbFohLXJ2mhMJp/09nSXKBMaSXRYEGIigkWxE2fz0TukW6otc3V2FC03NDWPsKZtG+5+OTs5DLMmEZHtyj6fiyee/Z0o5u7mio//8UezaxGJiMhyMpmc/67+I5oKVVVV2LJlC773ve8hPz9/2EJGQFyMOBn/Rip4rKysxLPPPot169bh5MmTkj0uk41tUIiIaFSuLs6i5W6JC4WWLl6Ik6fPQafTAwAMBgNOnDyNjetWS5oX2TfTwTwHFQcXrNHqlRkoKi03/uHQr9Hg2MnTWHfTSokzs1xYcIBoubKqRqJMppa6qla0rFQqEOTvI1E2RERT41LBFXz/qf8SxZycHPHRW3+Gt5enRFkREREREREREc1sphdZd3VN/YSvK9MXo7CozLjc2NyCz/Z/ja2bNmLJ4oU4fOyE8bbcvALcsGIZfHy8pzyviejo7MLOT74Uxfx9vZEcHytRRjReGq1OtOyg4iVl1mhN+ly8Wjk4ntbXr8GJcwVYs2zhKFvZhrAATxSpB7vU2ut4qOn9CvBxh5ODSqJsiIgmX1NzK7Y+8jOzieTf+ONzSIyLkiYpIiIiogk6cOAAnnjiCQAwXpts+nN4eDjc3d0RHh4OT8/Ba788PDxE+/Ly8jL+3NbWZnasjo6BZk/t7e3o7OyEWq1Ge3u7MX7NtULHiooKfPe738Wjjz6Kp556akL3z5rwTBQREY3KbCBPws6NAODm5oqF8+bg1Jnzxti5C7lYuWwp3NxcJcyM7JlGoxEtO3BwwSoFBvhhdnIiLuYXGmOnz+Vi6aL58PSw7dllI0ICRcvNrW3o6u6xuxntTAfzQoP8oVAoJMqGiGjydXR0YsuDPzLrLPzX3/8K82anSJQVERERERERERFNd+dGAJiTkoiYyDCUVlQZY4ePn8L82SlIWzQfmaeyjReFC4KAEyezcdvGdVOe13gJgoAPPv7cbJLcTRtuglwukygrGi/TAgQHFcdDrVFwgA9mxUcir6jCGDt14TLS5ibDw822xw0jAsXF2zOluDEiiBO9EpH90Ov1+M4P/w3qmjpR/KkfPIAtt66RKCsiIiKiiXn99dfxwgsvABgsZszIyEBGRgZmzZqF1NRUuLtP/bXJ1wod1Wo1Tpw4gf379xsLHgVBwGuvvQa1Wo0//vGPU57LVGLvVSIiGpUUA3nXk7FkEeTywY8wnU6Pk6fPSpgR2TsO5tmOG1csFRXD6fQ6nDqbI11CkyQ4wBdKpbjIT11tfwN6ZoN5JkWdRES2TBAEfO8n/4aiknJR/NEHt+Ohe7dIkxQREREREREREQEA3Nymf8JXuVyG+7dtgko5OC+5IAh4Z8enUCiUWDR/nmj9nIuX0NHROeV5jdeZnFxcuFQgii2Yk4wFcziZly3RaE3GQx04X761unHJbLPrJU5duCxhRpMjItBLtNze0Yl2K3zPs5TpeGh4oK9EmRARTb5fPv83fHX0pCi2culC/O9/Pi5RRkREREQTc+DAATz//PMQBAHu7u742c9+htOnT+ONN97AI488gvT09GkpbAQAd3d3pKSkYN26dXjuueeQnZ2N3bt3Y9u2bQAGzifu378fv/zlL6cln6nC4kYiIhqVtXVuBABPTw/MNelsc/rcBfT29o6wBdHEGQwGaHU6UYydG62Xt5cn5pu8P9TVN0mUzeRRKBQICwoQxexxttLKatPixoAR1iQisj3P//l17P7ioCi2aN5svPTb/5IoIyIiIiIiIiIiukaqCV8D/X1x+/obRbHG5hZ8/Pl+LE1bJJr4UK/XI/Nk9rTkNVbNLW3Y+cmXopibqwu237FBooxoojRak/FQTvZqtXy9PTAvOUYUq7aD8dAAH3c4qsRFtfY2HqrV6VBT1yCKsXMjEdmLL746gv9+8TVRLDjQHx++8jsolZw0gYiIiGyHWq3G448PTM6wbt06fPPNN3jkkUemrZhxLFJSUoyFjunp6RAEATt27MDJkyevv7GV4jdGIppGMgu3FyYli+lnwf2WWViDLhgs2RgA4OriLIp29/Ti+s/F1D9Xy5cuRs7FPGOb5/5+DbLP5mDV8gzLdmzpy1SyA1vymFv4fAkSHtsSY8xbq9GYrWt5caOtvp/ZBi9PD9GyacdDWxURGoDyqlrj8sW8y1h300oJM5pcNXUNaGpuFcXCQ8fbudHS9zMLPjctei8ELMpd0rxtlEyyD3zbJennvQXbW/J1GLDwtTK47eHjp/Dvzz0vutXH2wu73nwZTo4Owzy+Nvz9zJLNLfrVtPD32pLHzOL3FMkeNCIiIiKSBL/DDZDqceC5EJsyU89dzUSSvkaHHxPt6urGdL1n3Lh8CXIuFaKkXG2MHT95Bl4e7pg/dzZOn80xxs/mXMCKZUvh6uo6LbmNpqOzCy+/+tbV8eNBd2/eAHe3KcjP0teJQaJzZjbyXmbWuVGlsuwxt2BbmYXPtSC3YGzQoJ/4tpZe3zEOfj6eosdYoVBY+Jhb8JhN0utELpMhLNALJVWDhZp5hUWYnZI48f1bmctFpdDrxa+x8EBvibKZAIuuQZKQpO/Dljxmlryn2MZnz/Ak+l4oWPD+DwB6C7a35HfLks88AJN1XWNpRRUe+Ml/im5WKpXY8ff/RZC/9/CPr6WP+URZ/J5gwfNlyXcFS9O25PPa0sfMVs9JEBHRjPX88wPXeD366KN4+umnJc5mdB4eHnjzzTfx2muv4YUXXsAvfvELHDhwQOq0JoSdG4mIaFRmA3lW0LkRAHx9fTArOUEUO3n6HDQajUQZkb3SaLRmMQeVgwSZ0FiZPmf20mkzMTZStFxWoYa6unaEtW3PsUzxbNPuri4ICfCTKBsioslTXVuHux99AgbD4ECXTCbDe6+8gMjwUAkzIyIiIiIiIiKia9xMigVNC/amklwux31bb4fKpKPNFwe/hVYnQC4fvLRHq9Xh1Olz05bbSDq7uvGnV99GY1OLKL5gTjIWzEmRKCuyhEZj2rmR8+Vbs37T8VA7eb4Swv1Fy9lnL6Cvr1+ibCbfUZPx0IggHzg78doDIrJtvb19uOuRZ9DW3imK/+6/HsfyJfMlyoqIaIaRyfjv2j8iC3V2duLAgQNYtmyZ1Rc2DvXoo4/i4YcfRmVlJb766iup05kQFjcSEdGo3FxdRMvdPdZR3AgAKzKWiJZ7e/tw5vwFibIhe2U6SylgP8Vy9qrfpMjZQWUfz9fsxBh4uosvrjiWdVqibCZXX18/Tp0Vv38vWzQbCgX/XCEi2/ejZ36FhsZmUewX/+8nWHfTCokyIiIiIiIiIiIiU+YTvnZP6/ED/X1x39bbza7DO3j4BPq1eghDuqVknzmHvr6+ac1vqNa2dvzhr/9AdW29KB4c6I9777xFoqzIUmadGzkeatU0WtNiVPt4vtJnR4neB/v6+5F97qJ0CU2i5pZW5BUWiWLL58VLlA0R0eT5xfN/R07eZVFs++034/FH7pUoIyIiIqKJ27FjB2QyGX71q19Jncq4PfPMMwgLC8OXX34pdSoTwquFiYhoVK4mxY39/RrodLoR1p5egQH+SIyPFcWyss9aTX5kH0y7ACoUCigUComyobEw7eDq6Ggfs10qFAosWzRHFMs+ewG9vdJdwDBZss9dRF//4KyrMpkMyxbPljAjIqLJ0d7Ric/2fyOKbVyzCj9/+ocSZURERERERERERMNxc5Ouc+M1i+enYtumDWbxmvom5OZfhvbqGGhffz9Onz0/3ekBAIpLy/HCn19HfUOTKO7n442fPPIduJgUiZLtMCtutJNOgPbKtLjR0U6KUX09XZEaGyyKHc3KFhV426pjJ8+I7oeTowqLUqKkS4iIaBIIgoC3dnwmiiXHR+O1F56FjN2ziIiIyAZlZmZi3bp1CAsLkzqVCdm2bRuysrKkTmNCWNxIRESjMu3cCEgzmDeSFRlpouXOzi7kXMyTKBuyR5yl1PaYD77az3O2bPEcyOWDJ4A1Wi1OnsmRLqFJIAgCjmZli2JzkmLg4+UhUUZERJOn4EqJaFmhUOCff/s95HKejiEiIiIiIiIisiZSd268ZlXGYmzeuEYUc3Z2RldPH45lZqOpuQUAcDL7LLQm4yFTSaPRYNeevfjj395ES1u76DYfb0/89NHvwMvTfdryocklCILddgK0V+bPl/0Uo66cJ57guqa2HiXllRJlMzm0Oh0yT50VxZamxsDRwX6eNyKamRqaWtDU0iaK/fNPvx72ekMiIiIiW6BWq7Fs2TKp05iwZcuWoaOjQ+o0JoRX0xER0ahMB/IAoLu7R4JMhhcWGoLoyHBR7HhWNgwGg0QZkb0x7QLIgTzr199v8pw52EfnRgDw8nDD3OR4UWz/N0fQZUXvy+N18kwOamrrRbEVS+ZJkwwR0STLv1wsWo6LjoCPt5c0yRARERERERER0YjcXE06N3ZLN9nr2hsycPfmjRja7CY4OBi9ff04dfYCcvMvo62jA2fPX5iWfIpLy/HfL/wV3x47adY9LTDAF0//y0Pw8/Wellxoauj1erPxdU74at36NaYT9NpPkVxSVCD8vMTvyZ/t/dqmrwE5dDTTbDx3xYL4EdYmIrId+VdKRcvOTk6YNytRomyIiIiILKdWq222ayMAhIeHQxAEVFVVSZ3KuLG4kYiIRjXcTErWVkSzImOJaLmtvR25eQUSZUP2xrxQjgN51s6eOzcCwEqTwr/Orm7s/PRLaZKxUFt7Bz7as08UC/D1QmJMhEQZERFNLtPixllJvFiBiIiIiIiIiMgamU742t0j7XjoyvRF+O49W+B4dVzK1dUVnp6eAIDKqhoczczGPz/4CF1dU9dhsrKqBv/88BP88W9vovFqx8ihYqPC8dQPHoS3l8eU5UDTw7RQDrCvToD2yJ47bcplMqww6d5YXFaBIyeyJcrIMjV1DfjywLeiWEJkIIJ8PSXKiIho8pgWNybFRUGhUEiUDREREZHlPDxs+zyXu7u71ClMGIsbiYhoVA4ODlAqxQMX3T3SzVQ6nOioCISFBotix7OyzWYOJZqIiqpq0bKLs3k3U7IuGpMBWEdH++ncCADx0WGYnRQjip05n4uc3HyJMpoYQRDw3q496O3rE8Xv3LAKcrlshK2IiGxLwRVxcWNyYpxEmRARERERERER0Wjc3MRdwqxhstdF82bhP5/6ARJiowAMdG+8pq+vH6fPXcD3n/h3vP/RZ6iqqZuUY2q1Wpw8k4Pfvfwq/vfFv+PkmfNmY64qpRJbbl2LJ3/wANxNHjeyTeqaBtGyTCaDk52Nr9kb08leHe1sgt7lc2Pg6ymeiPvTvV+hoalZoowmRq/X458f7oZOrzfGZDIZbl81T7qkiIgmkWlxY0pCtESZEBHNcDIZ/137R2ShsLAw5Ofb1rW4Q6nVashkMpvsPsniRiIiui7TmUqtYTBvKJlMhhUZaaJYY1MzCk0uJicaL4PBgILLJaJYXEykRNnQWJkWN9rTTKXAwHve3bevhbOToyj+wcefW93782hOnslBXmGRKJY2Lxmzk2JH2IKIyPbkm3yPSElgcSMRERERERERkTWyts6N1/j5eOOnj96P7XdsgJ+vj9ns6xWVlTiWdRq//cNf8fs/vYYvDhzCxbxCtLV3jGkiWJ1Oh8qqGhw/eQbv7dqD//j1C/jnh7tRXlk17PrRkWH4jycexeqVSyGX85Ije5FfVC5ajgwNMpsAmayL1qxzo309X04OSty3fpEoptVq8e6OT2EwGCTKavy+PnIClVU1otjqxUmIDvGTKCMiosllWtyYnBAzwppEREREtiE8PByZmZlSpzFhBw4csNnuk/Z1ZoOIiKaEm6sL2js6jcvWMpg3VEJcLAID/FDf0GSMHT1xEkkJcZBxNg6aIHV1Lbq6u0WxlMR4ibKhserXaETLDnY2UykAeHm4YestN+GfH+8zxjq7uvHerj149IHtVn9BQX1jEz7as08U83R3xdZbbpQoIyLpCIKAto4uVNY0orK2EdV1zejp64dWp4NOp4dMJoODSgmVUgkfL3dEhPgjIiQAoYG+dnexgr3p6upGhVrcATolkQXcRERERERERETWyNVF3CHMmiYTlMtlWJWxGLOS4vDi397Cga8PGW/r7+9HS0sLfH19UVahRlmF2nibh7sbIsJC4OvjDZVKCQeVCnqDAVqtFn19/aiqqUNNbb2oo9hIVEolblt3I25asQRyOcde7YlOp8Pl0kpRLCU+SppkaMz6zSZ7tb/xgoSIAKycF4ujOYOTCBaXVeDrIydw840rJMxsbCqravDlgW9FsUAfD9y6Yo5EGRFJp7dPA3V9CyrrWqCua0ZrRw+0Oj10ej0MggCVUgGVQgFXF0eEB/ogIsgX4UG+8HJ3uf7OSVJmnRvjWdxIREREtm3Dhg148sknUV1djdDQUKnTGbdXX30VGRkZUqcxIfZ3ZoOIiCadtXduBK51b1yCjz790hirratHSWk54mKjJcyMbFm+SVe5wAA/+Pp4S5QNjZW9d268Jm1eMs7nXUZu4eDJ4guXCvDh7i9wz5bbrLawu6W1DS+/8jZ6+/pE8Xs2rYGLs5NEWRFNr7aObpy6UIjiilqoaxvR2d075m1P5hQCAOQyGYIDfBAR4o+5STGYFR9h9YXNM03BFXHXRrlcjgR+LyUiIiIiIiIiskpuruKL57u7eyAIglWda/fz8cZz//44HBTA6fMXUd/YBEEAamtr4ePjY5ZrR2cXLhVcseiYXp7uWL5kIZalzYenx7WukdfvCEm2o6SiRjRxqEwmQ3JclHQJ0Zho7Lxz4zWbVs1GXlkdmtsHJyT+9Muv4OHujqWL5kmX2HXUNzThz6+9Iyoel8lkuH/jUqiUCgkzI5oeWp0e5wsrkFdSjcraZjS0dl5/o6tyiwa7R3u4OiMi2BcJkUFYkhoDNxdeT2BNGptb0djcKoqlsHMjERER2bj169fjiSeewOOPP46PPvpI6nTG5fHHH0dnZye2b98udSoTYp9nNoiIaFK5ubqKlrt7xn4B/nRKSUqAj/cJtLS2GWPHsk6xuJEmRBAE5F8uFsVmJSVIlA2NlcFggFYnLm50dHSQKJupJZPJcM+mtSipeBs9vYOFgsdPnoGDSoUtt6+3qosuAKC1rR0vv/o2WtvaRfG0ecmYncRuZmTfBEFAUXkNjp6+hAsFpTAIll0AZBAEVNc3o7q+GVnnC+Hj6Y4Vi2YhfUEy3F2dr78DmnL5V8TfI2Iiw+HMIm4iIiIiIiIiIqvkalLcaDAY0NfXb3Xnc+RyObbecQsMBj16+/qhrq5FZVUt2tra4O09eRN0JsVHY2X6YsxOToBCwUnV7Fl+UZloOSIkEO5u7JRlzQRBgEYjLm50dLDPyV6dHJS4b/1CvLTjqCj+7s5P4aBSYcHcWRJlNrKm5ha8/Orb6OruFsVXL05CdKifRFkRTY+mtk4cP1+ErAvF6Ortt3h/Hd29uFRchUvFVfjs8DksSI7CygWJiArxs7prIWaigiJx10ZHRwfERNpedyMiIiIiU9u2bcPOnTvx5JNP4o9//KPU6YzJL37xCxw4cAARERFIT0+XOp0JseLiRkv++LB0ljgpj03jIVh4QTDRtLDkZIqkL/HBvIfv3DjK/bL0d1MwTGgzuQxYnp6Gz/YeNMYqKqtQUVmFyIgwy3KiqWHF7+Pq6hp0dnWJYimJcVd/st68RzMTPje1Wq1ZzMHBPosbAcDT3Q0Pb78Vf3vnE9HMn4eOZaGvvx/33nW71XRya2hqxsuvvC0qQAeAiNBAbL9t9WDAktepxZ8/lmwvzWffwLYSPmaW3G9L7rOl5NP3p3C/RousnEIcO52HuqbW628wQS3tndjzzUl8eTgb81NiccOS2YgKDZy8A1jyfBksfJ3JLZjF2OK3wInvwHSShJTE2Ol73VtyHA4G2xhLfr/4XBMRERFNPxm/c18j2blKKR9/+z8/O+ksGmPj4z1jWPxcD7zOTCd7BQYmfHV2tr6JxJIS4+Hv54vGpmYkxEYhLiYSgBxz5qRCXV0HdXUt+vo1193PUP6+3ggPDUZkWAjmpCQgMGC0Ahyp3kstfK4t+gy2YFtLP/un8JyiXq/H5dJKUSwlPmqS9m7JyVnLTuzKZBN/zAQbuL5DrzdcnUBxMFdHR0fILDmXbsn9tug1fv3nOiEiCLetSMXnxy4ZYwaDAf94dyf6NXcgffF8C44/uWpq6/Gn1/6J9g5xl7qEiADcuiIVwNXXpmAd47e2RaJxRUnHnyUcSx0HQRBQUFaLI+euIK+kZsreCnV6A7IvlSL7UinCA32wYkEC0lJjJq8bqqC//jqjMeiuv85UHNtg4f23YNzcdCw0MSYCSjnG/lhIeb2AJSz6rmDJGK6lr3WOKc4cfL5mIplMxsJ/gI8BTZpnnnkG+/btw/79+6FWq/HSSy8hNNQ6J3EoKCjAz3/+c+Tn50Mmk+Gll16SOqUJs+LiRiIishZuJjMzdndbZ+dGAJg7exYOH89Cx5CTxccyT7G4kcYtv1B8Ei7A3xd+vj4SZUNj1T/MYL2Dyj5nKr0mKS4S3912C17/8HNRAWtm9jm0tXfgvm13wMvTQ8IMgYt5hXhv1x50dolnKA0K8MWPHrgTTnbaXZPoSlk13v3sWzS3dV53XWcnJ4SHBiMiLAS+Pt5wUCmhVClhMAjQarXo7etDdU09KqtqUN/YNGLBuk5vwOncIpzOLcLyhSnYvDadv2MSKTArbowbYU0iIiIiIiIiIpKaq4t5p7qu7m6rHBuSyWRYsWwpdu/5EgAgl8kACFgwOxl33b4eBoOAxuYWVFbVoKqmDj29fdBqddDqdFDI5VApB849+np7IiI0GBGhwXBxsb4iTpp6pZU1ZoWwyXFR0iRDY9avGWayVzsfD123JBk9fVp8c/qyMSYIAt7Z8QmamluwYc0qKJXSXgZ54VIB3t35Kbp7xNfTRAb74Pt3rpi8AiwiK9Le1YsPD57GxaKq666rUCgQEhSAiLAQhAQFwsnRASqVCpABOq0OGq0WDU3NqKyqgbq6dthrP65R17fg/X0ncfhMIe67JQORwb6TebdojPKviLs/pyTESJQJERER0eRyd3fHSy+9hO9973vIy8vDmjVrsH37djzyyCMIC5O+HqGrqwuZmZnYsWMHMjMzAQz8jfzoo48iOTlZ4uwmjsWNRER0XaaDeQOdG62TQqHAsiWLse+rQ8ZYcWkZamrrEBIcJGFmZEsEQUD+5SJRLCUxXqJsaDw0w3ZutO/BPACYNyseD2zZgHd274NhSIe0/MvF+PXzf8bWTRuxZOHcaZ+dqKenFzv37EX22QtmtwX6++AnD90JN1deMEH2p0+jxZ6vsnD0TN6I6zioVFg0fzaS4mMRER4CPx/vMXda7evrR1VNLSrU1cg+dxHq6tph1zt+Nh95xZW47/YbkRQj/YmVmSb/SolomcWNRERERERERETWa7jiPmue8DU1JQnfHjmB1rY2Y+xY5knEREdCLpch0N8Xgf6+WDx/tvnG7GxKV+UXlYuWw4MD4eFu3sWUrMuw46Eq+74EUCaTYfOqOdBodTiWIz73vu/rI8jNv4z7t29GeGjwtOfW1d2DXZ/uxenzF81uCwvwwg+3roKTo/2PV9PMIggCTueXY9fXZ9HTN3IRYkJsNBbMTUVkeAhCggOhGmMRssFgQGPTwEQNlwqv4PyFPOj05t0Naxrb8Pzb+7A2fRY2LJvDIuJpVlAkLm5Mjo+WKBMiIiKiyZeRkYEXX3wRTzzxBGQyGXbs2IEdO3YgPDwc6enpSE1NRWpqKsLDw+Hm5jaluVRVVSE/Px9qtRp79+5Ffn6+8bZrDRK2b9+Op59+ekrzmGr2fWaDiIgmhenJpb7+fokyGZsF82bjaOZJdA8pwjyWeQrbt2ySMCuyJdU1dejo7BLFWJBgGzQmM5UqFUooFDPjBHbavGQ4OqrwxodfiE7s9/b24Z8f7sa5C5dw7123T1sXx4t5hfjg48/R3mHesS48JAA/evBOuLuaz4RNZOuu160x0N8PKzPSsGTRXLg4T6y418nJEXExUYiLicJNKzNQXlmFo5mncfbCJeh0OtG6re1d+NM7n7OL4zTr6elFWYV4ltqUhFiJsiEiIiIiIiIiouuRy+VQKpWi82u9fX0SZjQ6uVyO5Rlp+HzvQWOsrLwS6qpqhIeFSpgZ2Qq9Xo/CkgpRLCU+SppkaFw0WvE4gEwmg8rOixuBgfu5fc0CODmo8FV2oei2qpo6/N9Lr2D96pVYv3rltHVxvHCpAB98/LnZtQUAEBPqh3+5ayVcnDguQ/alvasXOw6exoURujU6OTli6aL5WJG+GMGB/hM6hlwuR2CAHwID/LB4wRzcdfsGZGWfw7Gs02hubROtaxAEHMi8hNyiKnZxnGZ5V0pFyyksbiQiIiI7s379enz88cd46KGH0Nk5cC1gZWUl1Go1du7cKVrXw8MDYWFh8PLyMsbc3d1F63h6ehp/bm9vF912bf9tbW3Gn9VqtVlO14oZZTKZ8efnnnsO27Ztm8hdtCr2f2aDiIgsVlldI1qe6Mmn6aJSqZCethBff3vMGCu4XITGpmb4+/EkFl1f/uVi0bKfrw9fOzbCdKbSmdC1cai5yXH40YNb8ObOL9HR1S267VLBFfz6+T9jZUYaVixdBB9vr0k/vsFgwOXiUhw+fgq5+ZeHXSc1MRoPbd0IZyfHST8+kZQEQcDeI2ew98iZYW+PjY7ArTffhIS46EntoiqTyRAdGY7oyHBsuX0dTpw6i/1fH0W/RjxL6vGz+SgoUeNH37kFgX7ek3Z8Gt7l4jLjCSRg4HlKio+RMCMiIiIiIiIiIhpNXX2j2cRhQQHWPSY6d/YsHD6Wic4hRTXHMk/h3m13SpgV2Yryqjr09oknNWZxo20wLW50UCknddzBmslkMtyxag7cXByx5+hFGAyD5+ENBgP2fnUYObn5WHvjciyYMwsq1eSPFQuCgOLSChw6loULlwqGXWdufCgeui3d7jtq0sxTWtWIv+8+iu5e80nx3Vxdceu6m5C2cA6cHCf3WgB3N1fcfNMKrLlhGfIKi/D5/m9QVVMnWudaF8etaxdj5cLEST0+mWtpbUddQ7MolpLA4kYiIiKyP7NmzcKhQ4fw+9//XlTQOPS6MGCgWLG9vd3iv89N92vqWlGjIAhISUnBb37zG6SkpFh0TGvBv6CJiOi6issqRctx0ZESZTJ2i+bPxfGsbPQNGZDJPnMet6xfI2FWZAsEQUD+5SJRLCUxbsYMCNm6/n5xMc9MK24EgISYcPz8pw9i15ff4vQF8YBab28fDnxzFAcPHcPslESszFiMpPhYyOVyi47Z09OLrDPncSzzNBqamoddx8nRAXdtvAFLF8zi7xPZHYMg4KN9x3Hk9CWz21QqFe7YuBarlqVZ/Lt2PW6urlh300osnDsb7+z8BEUl5aLbm9s68Yc3P8WP77sV4cHWfWGWrcszmSghKiIULi4T69RJRERERERERERTr7i0XLTs6OiAkOBAaZIZI6VSiYwli3Hg62+NsStFJWhuboGvr4+EmZEtyC8qEy2HBvnD08NNomxoPPo1JpO9zsACujWLE5EYEYB39mWjulHc7aKmrgFvf7AbH3+2HxlpC7B86WL4+Vo+6WNvXx+yz17E0cxs1NY3DLuOs6MKW9csQNqsKI6Hkt0pKKvFq58chUarN7ttwdxUbN98C9zdXKc0B7lcjtkpiUhJjMP+b45i39dHYDAYjLcbBAE7Dmajp0+DdRmp/D2cQgXF5aJllUqJuKhwaZIhIiIimmLu7u547rnn8Mwzz+Dvf/87du3ahY6ODgAY8Tvn9YoUR3K9/V0ranzmmWeQnp4+oWNYq5l3doOIiMals7ML9Q1NolhcTIRE2Yydk5MT0hbOx9ETJ42xK8Wl2CgIPHlFo6qta0Bbe4colpIYL1E2NF5mnRunYDZOW+Dq4oyHtm7E/Fnx+OCzr9HZ1SO6XRAEXMwrxMW8Qvj5eiMpPhYRYSGICAtBSFAAlMrR/0zo6u5BZVUNKqtqUKGuRv7lYmhNHvuhUuKjcO8da+Ht6T4p94/ImhgEAe99dhgncwrNbouLjsR92+9AwDR3//Xz9cbj338Ix7LO4NMvD4q6OHb19OHFtz/DT+6/FVGh1n1xli3Lv1wiWk5JiJUoEyIiIiIiIiIiGouS8grRcmx05JRPVjYZFs6fg2OZJ9HT02uMFZWUsbiRRmUwGFBQLH7Np8RFSZMMjdtwnRtnovBAb/y/B9Zif1YBDpzMF3VxBAbGMw9+exxfHT6B5IRYxMdGGcdDXV1crrt/rVaL6tp6VFbVoKyiCjmX8s0m2h0qNTYE96xbDC93TnRI9ie3uAqvfXIc+iGFhMDAxKt333krFsydNa35KBQK3HLzjZibmox/frjbrIvj50dz0K/VYdMN86c1r5nEdJKEhJgIqGbo5xERkVWQyQCZ9Z/DmHKyiRWTEY2Vu7s7nnnmGTzzzDPYv38/9u3bh6ysLGOh41CW1CoMVxiZkZGB9evXY8OGDXB3t8/rcPltkoiIRlVSLu7aKJPJEB1hGzMtpaYkiYob2zs60NjUjAB/PwmzImuXV3hFtOzr443AAL5mbIXGZKZSR0cHiTKxDnNT4hEbFYaPhunieE1TcyuON58xLisVCoQEB8LP1wcOKiWUSiUEQYBWq0Nffz+qaurQ0to2puM7Oznizg2rkM5ujWSnBEHAR/tPmBU2ymQybL7lZty0Ml2yC6DkcjlWLUvDrKR4vP7ODlRW1Rhv6+vX4K/vfYknHtqEkIDpLbycKQquiIsbkxPjJMqEiIiIiIiIiIjGorhUXOgVFx0pUSbj4+DggMT4OJy/kGuMFZWUYmnaQgmzImtXXlWLnt4+USwlIUqaZGjczCZ7dZiZk70CA+Oaty5Pxdz4ULyz95RZF0dgYCwn/3Ix8i8XG2O+3l6ICAuBt5cnlColVEol9Ho9tFodunt6UVVTi5q6BlFHuJGwWyPZuysV9Xj9U/PCxpTEODx4z5Yp79Y4mrCQIPzr49/HFwcO4cChY6LbDmZdgrOjCjenp0qUnX3Lv1IuWk6Jj5YmESIiIiKJrF+/HuvXrwcAdHZ2IjMzE7m5ucjPz0dbWxuqqqqGLXocjYeHBzw9PREeHo6wsDCkpqYiNTUVKSkpU3EXrA6LG4mIaFQl5WrRclhIEJycHCXKZnz8/Xzh4eGOjo5OY6y4tIzFjTSiawMbQ6UkxnEQwoYM7U4GzNzOjUO5Xe3iePPKNBzLvoDsnHz0jTKrqE6vN3ZlnKjgAF+sXDIXi+cmw9lGPjOIJmLf0bM4kp0risnlcjx07xYsmjdboqzE/Hy98cQPvou/vfkeikrKjfHu3n78+Z0v8PTDd8LXyz5nc5KS6feJWSxuJCIiIiIiIiKyasWl5aLluJgoSfKYiPjYaFFxY0VlFbRaLVQcI6ER5BeVi5ZDAv3g7ekhTTI0bv0mk73O1M6NQ4UHeuNfH7wZucU1OHa+GIUV9aOu39zahuYxTuY6kgBvd6yYH4els6Ph4jSzJ9wl+6Wub8Hfdx+BTi8ubFw0fzYevPtOKBQKiTIbpFAosGnjWnh4uGPXp3tFt+05fB6uzo5YNi9eouzsV4FJ58ZkFjcSERHRDObu7o5169Zh3bp1w97e2TlQx9DW1jbs7V5eXnbbjXE8eHaDiIhGVVxmm7OUAgNdk+Jjo3H2/EVjrLikHBlLFkuYFVmzuvpGtLaJZ3NMSeRJTluiMS1udOBA0jUhgX7YfttqbLp5BU5fKMDRUzmoqW+atP3L5XLMnxWPFWlzERcVyqJgsnvFFTXYe/i0KCaXy/H9h+7B7JREibIanpOTI370yP145c33RR0F27t68M6eQ/jpA7dDzt/ZSdPX1282QUgKixuJiIiIiIiIiKyaLY+JxkRHQi6XGzuM6XQ6lJVXIiE+VuLMyBoZDAYUFIlf7ynxUdIkQxOi0epEy5zsdYBCLse8hDDMSwhDfXMHjuUU4+SlcvT2a6+/8RjJZDLMiQvBygXxSIgM5NgK2TWNVoc3P8tEv0b8nrN00Xzct20T5HK5RJkN78blS+GgUuG9XXtE8Z0HsxEbFoAgP0+JMrNPeVdKRcspCSxuJCIiIhrJtcJFFjCOjsWNREQ0quJS8cBGbHSERJlMTFyMuLixQl0FjUbDgicaVv7lItGyt5cnggL9JcqGJkKjNZ2plIN5ppwcHbAibS6WL56D8qo6FJdXQV1Tj4rqejS1tI15PyqlAqFB/ogIDURESCBSEqLh6e46dYkTWZF+jRbv7PkWwpCYTCbDQ/dusbrCxmscVCo89uA9+NNrb6N0SOFdUXkNjp3Ow6q0VAmzsy9XSsqNF5Jdk8TZSomIiIiIiIiIrJYgCGadG2NtqLjRyckJYaHBqFRXG2PFpWUsbqRhVVbXo7u3VxRLjouSJhmaEPPiRl7+ZyrQ1wN3rV6A21bMwYWiKpTVNKOyrgXVje3Q6vRj3o8MQICPOyKCfBAR5I35CeHw9nABWNRIM8AXx3NR39Ihis2fnWKVhY3XLFuyEP39/fjos/3GmE5vwDtfZuLp+9dZbd62pr2jC9V1jaJYSnyMRNkQERERkb3g2Q0iIhpVSVmlaNmWZikFgJioCNFMpXq9HmUVlUiMZ/ccEhMEAfmXi0WxlMQ4dp+zMRqNuLjR0ZGFzCORyWSIDg9GdHiwMdbT2wd1TQOq6hrQ09sHrVYHrU4HuVwOlVIBlVIJHy8PRIQGIsjfFwoFT/7TzPT5oVNoahUP5t2xcS0WzZstUUZj4+jogB8+fB9++4e/oaW1zRj/9OsszIqPgJ+3h3TJ2ZH8K+LvExFhIXB3c5MoGyIiIiIiIiIiup6W1ja0d3SKYnExUdIkM0HxsTHi4saScumSIauWX1QuWg7y94WvN7tZ2RKNSRc1RwdO9joSRwcl0mZFIW1WFABArzegrrkDlfUtqG5oR59GC61OD53eAIVcdnU8VDFQ0Bjog7AALzg58vGlmae0uhGHTheKYpHhoXjoO3dZfYHgTSsz0NTShsPHTxpj5TVN+Ca7AGuXzpIwM/tRUFwuWlYoFIiPDpcmGSIiIiKyGyxunHSWFEAI11/FDgnCzLzfM5OtPteWFjZJeb8tyF02cDKquEzcuTEuJsp428gsvM+C4frrjESmEC06OjoiIjwU5RWDHYqKS8qnqLhx8j8DCi8XofBKMZydnREU6I+gwAD4+fpAoVCMZfPpYcnzZdmBJ3VvDY1NaG5pFcVSEuMm/Tg0tfr7NaJldm4cHxdnJyTGRiAx1sITzxZ9v7PR3zlLv9Nasr1F2459dtphGXTXX2fEY1v4+WHJ/bbgL+HiynocPpUrisVGRWD1qoyJ73QauTg7475tm/DyK28bYxqtDu/uOYSf3n8r5CMV9ev7J37Q6353vA654vrrjMTivzfHv31eoclkCQmx49+PBHlPxqYApDstYvF8FBbswNLnS7LJNKR8somIiIhoxpPqe7Ck45JSfoe20XNulrD0NcYx7AmQ6DU+CeeeisvVopBSqURERLhl56Wua3L/Lo+LjcY3h48Zl1taW9Hc3AJfX59hNpXw/WiY363u7h5kZZ9Bb28f/P18ERQYgKBAfzg5OUmQ4AgsOZduRe8ngiAgv6hMFEuJj5ImmdFY/B4+8fcFS95SBIOFYy5j1K8VT/bqYMvFjdP8fqRQyBEa4IXQAK9pPa45S8bILPnck+raDolZMp5psPQxk+gxt+B3S6PV4d29J0XXdCoVCjywfTNUStu43PiOjWuQX1iEhqZmY+yLozmYHROIIN/hJ3sVdH3TlZ4ZmdyCx9Xi6xTG/xrNv1IqWo6PDoOjSgGM+3NQou9Ilv79YMljzgn2bQufa7IlMhlfdwAfAyIbZxt/bRARkSR6e/tQVVMnisXF2FbnRmCgIHNocWNRSRkEQbD6jnxZp87gwDeHzeIKhWJgYC/A/+rgXgAC/f3g7Ow8/UnakfzLRaJlTw93hAQHSpQNTZTGngbziMjqaLQ6vPPZYdEwi0qpxH3b77D6WUqHSoqPxYr0xTiWddoYK6qoxfGz+Vi5iDOWWqrgcoloOTkxVqJMiIiIiIiIiIhoLIpLy0XL0ZHhUNrIxfvXBAUGwM3NFV1d3cZYcWn58MWNVqSzswtvvPMBWtvazW7z8vQYGAcN8B8YFw3wg5eXp9WP8VozdU09unp6RTGrLG6kUWm04kItB5VtvV8RkXXbeyIP9S3ijta3rLsRwUEBEmU0fg4ODrh/+x34w1/fMBZp6vQGvLP3FJ6+b83Ik73SmJhPlBAtUSZERERE9qmrqwtubm5SpzHteHaDiIhGVFahNovFRFrYzUsC8bEx+PrbwZlK29rb0dzSAj9fXwmzGt2F3LxhCxsBQK/Xo66+AXX1DUBu3kBQADw9PIzdHYMCAxAU4M8BvnHIv2zSZSkxjo+dDdJoxMWNjg4OEmVCRPbodG4xmlo7RLHbN6xGoL+fRBlN3OZbbkZeYRFaWtuMsX3HzmHZ/GQoFLZTqGmN8q+IixtnJU5Fx3AiIiIiIiIiIpospsWNcTFRkuRhCZlMhriYaORcvGSMFZeWYcniBRJmNbre3l68u+PjYQsbAaCtvQNt7R0ovHJ1DE8Q4OjoYJz49VqHxwB/P5srRpVKflG5aDnA1xt+Pl6S5EITx+JGIpoqXb39OHxOPDF4ZHgo1qxaJlFGExcbHYkbVyzFoaNZxlh5bQvyS2uRGhsiYWa2r8Dk+0QyJ0ogIiIimrCsrCxkZmYiMzMTVVVV6OgYvDbRw8MDnp6eSElJwbJly7Bhwwa7Lnrk2Q0iIhpRcVmFaDko0B9ubq4SZTNxAf5+cHd3Q2dnlzFWXFJutcWNl68UY8+XB8a9XXtHB9o7OnC5aPCCekdHRwQG+CEoIMBY+MgBPnONTc1obGoRxVJYiGCTNFqNaJmdG4losgiCgKNn8kWx6Mhw3LgiXaKMLOPk5Ij7tm3Cy6+8bYx1dPXiwuVyLEiJkTAz21ZSVomiUvF36BR2biQiIiIiIiIismqmxY2x0ZHSJGKhuJgoUXFjWXkltFotVCrrGyvRarV4f+cnqG9oHNd2/f0aVFRWoaKyyhiTy+Xw8/UWd3kMDICrq8tkp23TBEEwK25k10bbZFrc6MjxUCKaJCdzy6DV6Y3LCoUC92/fDIVCIWFWE3f7+tXIzb8suh7oWE4Jixst0NDUgpw8cQEsOzcSERERjU9VVRVee+017Ny50xi71nF8qPb2drS3t0OtVuPAgQN49tlnMWvWLPzmN79BUlLSdKY8LVjZQEREIzItboyz0YG8gZlKo3D+wuBgXlFJGZamLZQwq+GVV6ix69MvYDAYRPHI8DC0d3SirX34mUtH0t/fj0p1NSrV1cbYwACfz8DgXqC0A3zNLa04eeY8Ojq7oFQooVIN/Lv2s1KphEo50s8KqFQqKK/+r1IqjT+Pt+NiXqH4xJuHuxvCQoMn867SNOnvZ3EjEU2NsuoGVNU3i2Ib16yCXG67XQ6T4mMRGxWBkvJKY+zomTwWN05AXX0jfv2Hv+O1d3ZBr9eLbktOYHEjEREREREREZE1KzEdE7XBzo0AEBsTBZlMZrwYSqfToaKyCnGx1nXBuV6vx87dn0FdXSOKe7i7wdXVBQ2NTdDrDSNsbc5gMKChsRkNjc0ACoxxd3dXBAUEDEwEGxiAIH8f+Hh7Tfs5Xb1ej6wzF1Fd1wC5XD5kTFM5+LNSPEY6NK6U6c1jSsW470dVbQM6urpFMRYj2KZ+jVa07GCFBcxEZHsMgoBjOSWi2Pw5KQgJCpAoI8s5ODhg7Q3L8f5HnxljeSU1aG7vhq+n7U3uL6XOrm688OoHeOHVD9Dd0yu6jd8niIiIyF50dXVBrVYjOTl5yo7xi1/8wljUaFrQONK170PXu3TpEjZv3oyMjAw899xzCA0NnbJcpxuLG4mIaETFpfZR3AgA8bExouLGCnWV1c1UWltXjw92fQqdTjzTYsaSRVh70yrIZDL09vaivqEJdfUNqG9oRF1D48AAn04/wl7NDQzwNaGhsQm5eaMM8AUGTOkAn0ajwT8/3I32js5J37dCITcreBz4eWBA0MvTA8uXLoKXpwcAIP9ysWj7lMS4cRdIknXQaE0H8xwkyoSI7I1p10ZfH28k20GX35UZaaLixqKKWtQ2tiLY31vCrGxHR2cXfv+XN/Di3982G8gDBgobPT3cJciMiIiIiIiIiIjGyrRzo60WNzo7OyMsNATqqsFJT4tLy6yquFEQBHz6xT4UlZSJ4u5ubvje/ffAy8sTer0eTc0tg+Oh9Q2oq29ET0/PuI7V2dmNzs6ywWMJBqhUyoGx0GtjogH+CAzwg4PD1I0nHTyShVPnLl1/xZEIumHDCrniakHktUJJxZDJYhVwcXbC/ORYREcMdKcy7dro5+0Ff1+viedFktGajYfy8j8islxheR2a2rpEsVUZaRJlM3kWz5+D3V8cQF9fPwBAAHA8pwSbVs2RNjEb0d+vwSvvfYr//tNbaGxuM7vdw90ViTER058YERGJyGQyXu+KkQvDiMbqxIkTeOKJJ/Dyyy9j7dq1k7rvgoICPP7441Cr1cZixbG+Zk3XEwQBJ06cwJo1a/Dmm29i6dKlk5qrVHh2g4iIRmQ6S2msDRc3xkRFQC6XGzsi6nQ6lJVXIiHeOjrpNDe34N0PP0a/pl8Unz8n1VjYCAwMSkZFhiMqMty4jl6vR1PT0AG+RtQ1NKBnmAvsR2M2wAdcHeDzR2R4GFYuWwJHR0cL7qXYmfO5U1LYCAB6vQF6fT/60D/iOleKy/D9h+5Bb1/f1ZlcB6UkxU9JXjT1NCYzlTo6SlvcaDAYUFJWib7+fsRFR8LZ2UnSfIhoYjq7e3E+v1QUW5m+2Ka7Nl4zb04K3Pa4oqt7cMbuY2fzsW39Mgmzsn79/Rr89c0P8NsXX0FzS9uw63h5euDP//df05sYERERERERERGNS3t7BxqbxONEtlrcCAzkPrS4sai4FOvX3iRhRoMEQcC+g4eQm1coijs7OeH+e+6Cl5cnAEChUCAwwB+BAf6ibTs7OgYKHRsajWOiLa1tZrPcj0ar1aGqug5V1XXGmEwmg4+3J4KDApC+eCFCQ4IsvKeD2to7cDonb9L2N5TeoIe+f/QJcHMLinD/nesQHR5iVtw4KyGaF37aKI1WXPDq6CDthM56vR7FFTXQ6fSIjQyBk8Tjs0Q0MUfPiScFDw0OREyU7RetOTo6YOmi+Th8/KQxlnmxFBuXzYJKqZAwM+tmMBjw/qcH8ewLr6FcXTvsOg4OKvzpuafh5DR515IRERERSWndunUQBAE//elPJ7VoMD8/H1u2bDEuy2QyCIIw7DktDw8PeHp6GpfVarXZOtfO5wiCgO9+97t47rnnsHXr1knJVUosbiQiohEN7eIDALHRtnvSysnJCeFhIaiorDLGikvLraK4saOjE+98+DG6TWYbTUqIw20bb77uoNKIA3ydXQMFj41NV2c0bZjgAF/twL+aWjxwz11QKCw/uafVapGZfdbi/Viio7MLn3x5EOGhwaK4u5urWYxsgyAIZp0bP/j486uz7voi0H9gBl5/Px8olVP7NVin0yEntwAnss+ita0dAKBUKJGSGIdF82cjPDSYA8ZENuT0pWLo9AbjslKpRHrafAkzmjwqpRLLlizAgUPHjLFTF6/gzrVLoZyEz3x7o9fr8e6uz/GL3/0JlVXDD+Q5Ojrgxw9/B//+xGPw8faa3gSJiIiIiIiIiGhcTCd7lcvliIoIkygby8XHxeDboyeMy80trWhtbYO3FZynOno8C9lnz4tiKpUS927bjAB/v1G3lclk8PBwh4eHu2h8V6PRiCZ/rW9oQn1DI7Ta4bsdDkcQBDS3tKG5pQ2FV0rwvfu2IyQ4cHx3bgQnss/DYBj72OxkEwQBH+89gtvXLkd7p7gbV0p8lDRJkcVMixs/2n8Mgf6+CPLzRqCfNwL9vODv4wXlFBftaLU6nMsrQua5fLR3DkygqFIpMTsxGgtT4xES6MfxUCIb0dHdh0ul4nGvlRlpdvM7vDJjsai4sau3H7nFNViQFD7KVjOTIAjY920W/uP//o6LBcXDriOXy/HAlvX45VOPIiJ08iaFICIiIrIG27Ztw86dO/H444/jm2++gZubm0X7G1rYeO36fQ8PD6Snp2PZsmVITU1FeHg43N3dr7ufS5cu4cSJEzhw4ACAwSLJZ599Fl5eXpPebXK6sbiRiIiGpdFoUF5ZLYrF2XDnRgCIi4k2KW4sG2Xt6dHb24t3d3yMtvZ2UTwqIhx33XHrhDtCjWWAr77h2iDf2Ab4KiqrsO/gIdy6wfIvP+cv5ok6RAHA0kXzIZPJoNProNUO/Bv8WQudXj8Q012N6bTQ6fTGbpwTUVxagfMX8+Hu5mqMJSfG2s0J2pnGYDCYvR56+/pQVqlGWeXg7CVyuRy+3l5Xix79EOjvh0B/X7i7u1n83Gs0Gpy9cAmZ2efQ2SV+jev0OlzML8TF/EL4+/pg4dxUzE1NZjdHIhtQUlknWl4wZxbcXF1HWNv2LF+6WFTc2NevRU1DKyKCR7+gaCYRBAFffnUE//Hff8SlgqJh15HL5Xjw7jvwy2d+NDBRAr9PEBERERERERFZveLSctFyeFgIHB1tt/tMcFAgXF1d0N09OKlqUUkp0hYtkDArIPvMOVHRJQAoFHJsv3MTwsNCJ7xfBwcHhIeFivZhMBjQ0tpmnPy1rr4R9Y2N6OzovO7+dDo9Pvz4Mzz20L1wc7PsHHBHZxfOXcwXxWIiQxHk7wet7uqYp+7amKj+6vinDjqdfkhcB63GfPxrPLp7e/H6h5/DxdnROA7m6+WJAD9vi+4fSUMQBOh04o6dPb39KFPXoUw9OJYhl8vg5+2JQD8vBPp5Gwsf3VydLR4P7e/X4HTuZWSdy0d3bx+Awf1ptTqcu1SEc5eKEOTvg4Wp8ZiTFANHdnMksmplNc2iidIdHRyweMEcCTOaXEEB/oiPjUJRSbkxVlrdxOJGEyfPXcK//c9fcfRUzojr3L52Of77//0AsxJjMPT9n4iIiMhebNiwATt37kRHRwceeughfPTRRxbt7/HHHzd+187IyMCjjz6K9PT0ce8nJSUFKSkp2LZtGzo7O/H3v/8d//jHP4wFjj/96U/xySefICkpyaJ8pcTiRiIiGlaFusZskMSWOzcCQFxMFL45PHjhfktrG5qbW+Dr6yNJPhqNBu/t2I2GxiZRPDgoEPdsvWNKOssNP8CnNw7wDZ3ZtLOz22z7M+cvIigwAIsWzJ1wDnq9HsdPnhHFYqMjsX7NqnHuSTDuz1j0qLtaCHl10G+wEHLw52NZp41FZz29vTh74RLmpSbDy9MDAJCSGD/h+0bSUigU8Pf1QWNzy6jrGQwGNDa3oLG5BbkFl41xF2fngULHAN+Bwkd/vzF3eezr60f2uQs4eSYHPb29112/sbkF+w8dxddHMjErKR4L56WymyORFausFX9WJ8ZFS5TJ1PD18TJ7/1TXNrK48arM7PP4t1+/gOOnzo24zh0bVuM3//E4UhLjpjEzIiIiIiIiIiKylGlxY1x0lCR5TBaZTIbY6ChcvDRYVFdcWi5pcWPupQLsO3hIFJPJZNh820bExU7+uVa5XA4/Xx/4+fogNWXwgq6ujnbUNzYZCx7rGhrR3NJmNibe0dmFHbs/x4P33mXReG3m6fPQ6wf3rVDIccf6G+HhPs4Z//X90Ov1Q8Y/9dctiiwoLkeZumZgewG4dLkEAX7eiA4PATDQtZFjUrZJJpMhwM8L9Y2to65nMAhoaG5DQ3Mbci+XG+Muzo6iYkdjl0fV9bs89vT24VROIU5dKEBfv+a669c1tuDLb0/hq+NnkcpujkRWTV0nvsYiKiIUTjY82cNwEuNiRMWNlfWjX1cykxQUlePnv38Fn+w/MuI6yxfPxf/8279g2WL7KXolIiIiGk56ejo8PDzQ2dmJvLw8vPHGG/je9743oX298MILUKvV8PDwwK9//WusW7duUnJ0d3fHM888g+9///t46KGHkJ+fD0EQ8POf/9ziYkwpsbiRiIiGVVxWIVr29fGGt5enRNlMjqDAALi5uaJrSDe14tJySYob9Xo9du7+DFU1taK4r4837tu+ZVpnhB1pgK+7uweVVdXY/dleUWfHvQcPwd/PF5ERYRM63oVLBejoFM+MujIjbWLJY6CgTaFQABjbYxbg74u3P9gNQRDQ1NwKCALyLxdj0bxUeHt5IiIsZMK5kPS2btqIg98eQ2VVDTRa7bi27entHVOXx6AAP7i5uUImk6G7uwcnz+Qg+9wF9GtGHsS7NjuKKZ1ehwt5BbiQV8BujkRWqqunDy3tXaJYuB1+VoSHhYiKGytrm7BMwnysQV5hMf7zf17CZ/u/HXGdlemL8D8/fwrpi+dNX2JERERERERERDRpSkzGRONioqRJZBLFx8aIihvLyiuh0+mmZGLV6ykuKcMnn+81GyPZePNq0bjkdHBzc4WbmytioyONMa1Wi8amFhz45ggq1NXGuLq6FnsPfovbNqyZUCFWd3cPzl4Qd22cn5o0/sLGq66Nh461+11qUixeefcTtHd0oKu7B719GlRW18PT3Q0+Xh5IiY+aUB5kHbasW46Dx8+hsrphAuOhI3R59PG6WuzojSA/H1GXx86uHmSdz8eZ3CujHm+k8VDNkG6OwQE+WJiagNmJ0ezmSGRFKurFBdP2OBZqei2Qun5gggO5XC5RRtKrqm3AL196C2/t/HLELtGzEqLx23/9F9y6ZhmL04mIiGjGWL9+PXbu3AkAeOWVVyZc3Pjaa6/B09MTH330EcLDJ79ruIeHB3bv3o1nn30WO3fuRF5eHr766iusXbt20o81HVjcSEREwyouNRnIs/GujcDAyfS4mCjkXMwzxopLy7Fk8fTOVGowGPDJ5/vMZoL1cHfH/XffBVdXl2nNZySuri5ITozH7Rtvxsd79hrjBoMBOz/5HI8+eC+8xlnwajAYcCwrWxSLDA9FZHjoCFtMvqiIMNy4YikOHc1CY1MznJ2d4ezkgJxLhXj4vrtm9IlLexDg74v7tt0BQRDQ1t6BuvpG1Dc2ob6xGfUNTWhpaxvX/kbr8ujv64OaugZodSMP4kVHhGNF+iIE+vvhQl4hzuZcQnPr8DOpspsjkXWqrGkULcvlclzIzUdufiFUShVUKiU8PdwRERYCby9Pm/2djQgLxrkLl4zLpt0qZ5LKqlr88vd/wT93fjbiQN7s5AT8z389iQ2rV9rsc05EREREREREREBxWbloOS4mcvgVbUhsTKSoyEir1aKisgqx01y4qa6qxo6P95idY7txZQYWL5w3rbmMRKVSISQ4ENs234rX3v4Abe0dxtvOXbiEoEB/pE0g16wzF0STx8rlMixPmz8ZKY+Ji7MT7rrlJry54zM0tbYjLioUjg4qlFbWwM/bE0EBvtOWC00+fx9PfOf2GwfGQzu6UNfYioaWDtQ3t6K+qQ0tbZ3X38kQ4i6PZca4i5MT/Hw8UFPfDJ1eP+L2cVEhWLF4Dnw83ZGTX4yzl4rQ1tE17Lq1DS344tBJHDx2BqmJ0Vg0JxkhgX7jypeIJpfBYEBFTbMoVt/QhI/27INSqYBSqYSzkyOCgwIRERYCNyu5pmm8TIsbNVodGlq7EOTrIVFG0mlp68D//u1d/Pmtj0fsxBsRGohfPfUo7rtz3dUJ54mIyKrJ5ACvewVk5pOtEE1Eamqqsbixo6MDu3btwtatW8e1j2vbv/jii1NS2DjUc889B7VajaysLDz//PMsbiQiIvtSUl4pWh46g6Uti4uJFhU3llVUQqvVQqVSTcvxBUHAvoOHcCm/UBR3dnLC/XffNe5iwekwe1Yy6hsacTzrtDHW3d2DDz/eg4cfuAeqcfxNdCn/Mlrb2kUxS7o2TtSK9MWorKqBh4cbZJDhyIlT6O7uRm7BFaxetQxenjPv5KW9kclk8PbyhLeXJ5IT44zx/v5+NDQ1o76hGXUNVwsfG5om1OWxoqp6xNsTYqOxIn0RwkMHT5BnpC1A+uL5KK+swtkLl1BwpQT6YQYCh3ZzDPDzxcK5qZgzK4ndHImmSXtXD4rKa1FZ1wR1bSNKqxpEtxsMBnz51eFht3VzdUFEWAjCw0IQERaChNgouLrYxgBfZJh4ooGahoGLFZQzaLCquaUNv33pNfz1zQ/QP8JAXmR4CJ7715/i3i23cCCPiIiIiIiIiMgOmE5Gag+dG11cXBAaEoyq6hpjrLi0bFqLG+vrG/Hejt3Qmoy/LFk0HyuXpU9bHmPl6uqCu7fchn+8s0NUlLj/6yPw9/VBVIjPmPfV29uH7PO5otjclAR4ebpPWr5jER4SiDXLFuGLvn50dvdAq9Ohu6cPZepaFJWpkRBj+5Mbz3QymQzenu7w9nRHinzwfHV/vwYNzW2ob25DXWML6pvaUN/UCs2Q1/ZY9PT1obKmb8Tbk2MjsGLxbIQE+RtjK9LmYPni2SipqMHZS1dwuVQNg2GUbo55xQgO8L3azTGG3RyJpkG/RoeC8jpU1LWg8uq/nj7x53Vu/uURtgZ8vb2M46ExUeGIi460iUnEPdzd4OXpIZrIoLKuZUYVN/b09uHlNz/C7/7+3ohF6L7enviPnzyIf7lvM5ycHKc5QyIiIiLrkJqaCgDGycM+/PDDcRc37t+/H8uWLUN6+vScB3v55ZexevVqVFZWoqCgAMnJydNy3MnE4karYmm3B1ab03Tg62z8LPndtvDxlk/8guuSMtPOjZGQycZ6MsrCvIXhO+RMxraxUeGQyWCcqVSn06KiohJxsdEDK4z5Pk7M4WOZOH0uRxRzUKnwne13wt/fghkyLXmZDTOYYOqmlctQ39CIopLBmRrr6huw54v9uPOW1WPqWGQwGHDkRBaEIc9RaHAgoiPDRLHpsvmWm/G3N99DZvY5tLV3wNnJCQa9Aa//cwe+s3UTgoMCpuzY115/NP0cHR0RHhoiKjoUBAGtbe2ob2gydnmsa2g0K8S9HplMhpTEOKxYuhhBgf4jrhMdGY7oyHB0d/cg51IBzuZcGrGjZENTM/Z9cwRfHT6B+XNSsO6mFVAqp+ErPF+jNsaC58vS91/98AVgY2IY3yD6pG5vsq0gCLhSUY+vswtQUFY74Ue0q7sH+ZeLkX+5GACgVCqxaF4qVmakITI81Ko7/IWHBYuWdXoD6uvrERrgDQAQ+tomvG+ZwsKLERQWDJyNYVa23t4+/OHv/8Tv//ImOjqHH8jz8/XGfz75ffzgwe1DLq64zr4teiu19Pu0Bdtb/DK13tf56Cx5zC28zxY9X1I+3hI+ZkREREQ0SWz1+6QFLP0ObbPnzWbgc00zhiXn3Lq7e1BTWy+KxUZHYcy/Mxa9J0zt9SFxMVHi4saSMqxbfcPVQ0/te0Jraxve3fER+vrERVFzUlOwfu1NFjxnlr6Hj34+PCjAD3dsXItde/YaYwaDHrs+/QKPbL8V3l5jK344deY8NP39xmWZTMDyxXMAYeTOd1MlfdEcVNY24Kuj2aisrsecpFi4ujjjw88P4dbVy7BgduLoO7DoNW7B+IOU57wEC64XsLRLiAVjNkPHnx0cVAgL9kdYsL/o9mtdHq91eKxvbEVrx9Uuj8LYHnOZTIbZiVFYvigVAb4D4wcw6c4qAxAXEYy4iGB0dvXg/NVuju2d3cPtELUNzfjiUBYOHjuDxXOSsHrZAusvlLL0O6Fkr3FLH9fpv65j8NDSHFswjG+SZDOWjGda8jobZtva5g58c6YEZy9XQaub+OPZ3NqG5tY2nM/NBzAwhrZi6WKkp82Hm6vrhPc7HSLCQkTFjVV1zVicGDSw0Nti2c5lFkyKqrBgQv4xHNdgMOAfO/biVy+9hZr6pmHXcXF2wpOPbMfPHr0bnh5uVze8zncXKx77Hp2FeVu0uSWfH5b+bW2rzxeNH8/DEBFZyrTTYn5+/rj3oVar8dhjj01WStfl7u6OrVu34h//+Af27dvH4kYiIrIfxabFjXYwSykAODs7IywkGOohg3lFpWWDxY1T6GT2WRw5niWKKRRybN+yCWFDCq2skVwux5bbN+L1f36ApubBE3qXCi4jwNcTK9IXX3cfBVeK0dTSKoqtTE+TrNDD1dUFWzdtRH5hMRxUKkSGhUIul6Oruwdvvv8Rtm++xW46ltLoZDIZfLy94OPtNWyXx7r6a0WPw3d5lMvlmJOShOVLF8LPd+wz97q6umDZkoXISFswpm6Op89fREdnF+6+81arLpAishU9fRpcLKpCdl4ZyqqboNFN/oUlOp0OJ8/k4OSZHESEhWBlRhoWzUuFg4P1zTzs4uwMZycn9A654Ki338IBWxtgMBhwy70/xOHM08Pe7urijKf+5SE8/cOH4OHuNs3ZERERERERERHRVCqtUJvFYqLsY2woLiYKh49lGpcbm5rR1tYOLy/PKT1uV1c33vngI3SaTCKWEB+LTbeut/rxjVnJCahvbMLRzGxjrKe3Dx/u2Y+H79kMB4fRiw/6+zU4efaiKJaaGAtfH6+pSPe6ZDIZ7ly/Chfzi9HZ1QPIZHBwUMFgMOCzr46ho6sbq5bOt/rnhSw3tMtjctxg187+fg0aWtrR0Nw+UPjY1Dpsl0eFQo55ybFYtnAWfMZY6AsA7m4uWJk2B8sXpaKksgZnLxXhSlnVCN0ctThxNhe9ff24fe2yid9ZIgIwMJFpXmk9sgvUKKpqMuvOOFmamlvxyZcH8fmBQ1g4NxUrMxYjKiLMKj9bfLzF34N6NfY/FgoAP3r2Rbzy3mfD3qZUKvDoPbfjv37yIIICLJiUn4iIiMiOuLu7m8WysrLG1YVRrVYbO0BOl7vvvhv/+Mc/cOLECTz11FPTeuzJwOJGIiIyo9frUVouHsyLs6Mir7iYKFFxY3Fp+ZQf8+KlfOz/+ltRTCaT4c7bb0FsTBRsYcYcJycn3L1lE15/+330DZlt9NCxLAT6+yEhbuQCUUEQcNSkaCAowH/UbaZDZHgoXvztz/Hurj1oaGw2xjUaLd7b9Rk2bVyDuam2N3sFTY7rdXlsbG6Bo4MDEuNj4OU59kE8U+Pp5ni5uBT5l4swKylhwscjmukq6lpxNKcM2fnlww6eT5XKqhq8u/NTfPLFAdxxy83ISFtgdYN6KpUSvUMmU9fqLOyuaQMOnzg9bGGjSqXEY/dvxc+f+gECA/wkyIyIiIiIiIiIiKZaiclkryHBgXB1dZEom8kVEhwEF2dn9PT2GmPFpWVYtGDelB2zr68P7374EVpaxZOdRoSHYuvm26BQKGyiA+6NK9JR39CEy8Wlxlh9YzM+3X8IW2+7edTzuqdzLqGvXyOKrVgyf8pyHQsHBwf8+pnHsOfgMVwsKBbddjjrHDo6u3HrmmXW3ymPpoSjowPCg/0RERJkjA12eWxBY0s7nJ0ckRgdBg/3iXdkk8vliI8KQ3xUGDo6u3E+vxjn8orR3t1rtu65vCuYnRSD6PDgCR+PaCZr7ezFifxSHM0pQ880TmSq0+lw6mwOTp3NQVJCLL5z1ybJivtHolKKJynQTcHkt9amqrYBr33wxbC33X3bTXju6UcRFxU2zVkRERER2Z6qqiqpU7iuax0nOzs7Jc5kYnhmioiIzKira6E16UwWF2M/xY3xJl0am1ta0draNmXHu1JUgk+/2G8Wv3X9GsxKTpyy404FP18fbNm0UTRoJwgCPv58Pxqbmkfc7kpxGeobm0SxFemLrKKow8PDHd/7zlZERYSK4gaDAZ98cRDHsk5DsIGBVpoe17o8JifGYWVGGpYsmmdRYaOpa90cf/LYA3hg+2bMSoofGOwfYt/XR9HX1z/CHohoOBqtHlmXyvG7dw/hd+8ewslLZVNW2Ojo4ABfH28oRrgQpLunF+/t2oO/vP4OWtrapySHiVIpxfMfzYQBvX+897FZ7N47N6Lg+Gf40//8BwsbiYiIiIiIiIjsWHGpuLgxLiZKmkSmgFwuR6zJ+G5RSdmUHU+r1eL9nbtRV98gigcG+OPebXdCpRq946E1kclkuPO2dfD39RHF86+U4mjW2RG302i0yDxzQRRLjo9GgJ/PCFtMH4VCgc3rV2H54rlmt527dBkf7PkKmhnSvYqu71qXx+S4SKxMm4PFcxItKmw05eHuilVL5uLxhzbjO5vWIik2wuy6gS++yYJuBkzASDRZDIKAgsomvPrFOTz71hHsP3VlSgsbPd1c4DXK+0LhlRL85oU/W931Nkql+NoLrc4gUSbT562P9sNgEN/Pm1csxpnPX8X7Lz/LwkYiIiKiYQxXHNjR0TGufYSHh0OtVl9/xUkm1XEnA4sbiYjITHFZpWjZw90Nfr7SD7pMluCgQLi6iGddLSqdmsG8iko1dn7ymdmJotWrlmPhfPPBI1sQHxuDNTesEMX6NRp8uPtL9A5t93SVIAg4mpUtivn5eCM5IW5K8xwPJydH3LftDqQmm3fD++ZIJr48+K3Zc0g0lWQyGWKiIrB100bceevNotu6urvxzdFMiTIjsi2CICA7//+zd9fhcZ1n+vjvATEzM0uWbdlCM1NiJ4bE4aSBNqWk3bb73W5ht7vZ7m+3zbZNuWE2xrGTmFG2JUu2JVvMzMw09PvD8dhnRrIlzUhHI92f6/KVnOfQMyMN6Lzned4a/OIfx/DR8euobuq87z421tYICwnE/JhILF44D/ELY7FgXhSiI0L1bmYZzfDICNo7OqEBEBLkDydHh1G3Kyguw2u//RMuZ1yfMYN6Kp3Putneqbu9owsHvzotiP3vv/0IH/31fxAc6CdSVkRERERERERENF3KdGZuDA0KFCeRKRIaLGz4WllVA5XK+A3NVCoV9h/6AjW19YK4s5MTnn78EVhaWhr9nFPNwsICj+/aBksLC0H8XNpVFJWOPq6clVuIAZ2x0uVJi6Ysx4mSSCRYtzwBD6xdoldIVlpZi/f2f4X+Af1Z9IimilQqRViQLx7buhZbViUL1rV3dePi1VyRMiMyLY3tvXh93xX8+fNryKloue8kydaWFogI8kFibBiWLorEyoQYLFsUhaT54YgJ9YOD7f1nse7uG0BXbz8c7KwREegNuUx/THF4eASfHvwCf/zH+2jv6JrkozMu3ft+pFLxG7JPJbVajXf2HRXEvvHoFhz/4DdYNE///igiIjJhEgn/3f5HZARdXV3jit2LmEWG9vbGm7BlOsnvvwkREc015TrFjSFBATNihj1jkUgkCA0OxM28Am2srKIKiYvjjHqepuYWfLr/c71Zj1ISF2PZkiSjnmu6LUmKR1NzC3ILirSx9s5OHPziOJ7YtU1QDFFRVYP6xmbB/iuWJM64ggm5XI6d2zbB3s4WaZlZgnXXsnPR29eHXds2m1RnWZodoiPCEB5ShJK7Oipfu5GL+TER8PPxFjEzopmtu38Ie05lIaesccxtZDIZwkMC4e/nA39fb/j7eMPZyeGe33sGBgZRU9+AmroG1NQ1oqSsAn39A3rbqdVqlFfWwM3VGQ8/sB4lZZUoKC4TbDM0NIyP9x9Gdk4+nntiF2xt7j9YOJUUCmEXZDOd7qWzzccHvxJ0Izc3N8Nzux8SMSMiIiIiIiIiIppOusWNIbO8uHFEoUBNbT2CggLG2GPiNBoNDn95HCWl5YK4ra0Nnn58F2xtjTfb23RzdnLEIw9vwUf7PsfddSKfHT2DF5/cDndXF21MqVTi8tVswf5hQf7w9nQH1DNrRsSEBdGws7HGgaPnBOPYDc1teOvTI3hqxya4OI3etI9oqiyODcfNwjLUNbVqY5eu5iI2Igiuzo7iJUY0g6nUapy+XoljGWVQqseuaAzy9UCovyf8vdzg7+UKF0e7+94D1tXbj9rGNtQ0tqKyrgVFlfWjNmvt7h1Ad+8AkheEw8XBDpeyC9HdKxw3LSqtwH+9/mc8vnMrEhbNn9yDNRKFzoyw5rN8LPRsWhaq6poEsZef2CZSNkRERESmo66uTi/m6Og4oWMsWbIEaWlpeOGFF4yU1fh0d3dPONeZYmZVFRAR0Yyg36XUeANcM0VocKBgubKqFkqdi1iG6OjoxEd7DmJoeFgQXxgbgw1rV5l8sahEIsG2LRvg7ekhiJdVVuP0hcuCWGraVcGyk6PDqDMkzgQSiQQb1izHprUr9H5GxaWVeP/TzzDAjqU0zSQSCbasXwUz+Z3CWo1Ggy+On52SDstEpu72bI2vvXtyzMJGJ0cHbNu8Fv/18x/h+998Fg9tXoe42Gi4ODve9zPa2toKkWEh2LB6OV58+lH81y9+jOee2ImQQP9Rt29t68Dho6fh5eGOF5/ZDQd7O71tCorL8Lu/vI2u7p6JP2AjUalUGNb53jKbixs1Gg3e+uiAILZjyzq4ujiJlBEREREREREREU23sgqdMVGd8UNTZ2NjrTeWV1peYbTjazQanDh9Djl3NZQFAEtLSzz92CNwcnI02rnEEhIUgA2rlwtiIwoFPj10XDBmmJ1XhN4+YTHHiuTF05LjZESGBuLZXVtgZSmcmbKzuxdv7/kCdY0tImVGc5VUKsXWtUsEDZJVahW+OJM+akEV0Vx3e7bGL9JLRy1stDA3w4r4aPzsW7vw4288hIfXJmFRdDBcnezHdb+So50NYsMD8MDKeHzvyS34j+8/jk3L4mBnYzXq9lduluBKTgke37IcSxZG6K0fGh7Gu58cwLmLVyb+YI1oYEA4w/JsHgsFgLf3fiVYnh8Zgvj5+j8fIiIiIhIabcbFic6GmJKSgrS0NPT19Rkrrfvq7e1FT08P/Pz8pu2cxsTiRiIi0qM7c2No8OwrbgwJChRcsFMoFaiu0e+0MBm9vX34cM8B9PX3C+IRYSHY9sBGky9svM3MzAyP7XwItjbCjqtpmVnIyb81o2N1bT2q6+oF65cnx8+4WRt1JSfEYde2TZDJhHnWNTTh7Y/2obOrW6TMaK5ydLDH6uXJglhLWzvSdboAE811g8MKvHnkCt4/ehUDQ/rdsCPDgvHyN57Af/7rD7Fp7UrY29kafE4zuRyJixbgR997Ef/6T9/BsuR4yOVywTYajQZnUtNw5OhpvPDUI0iO158turG5Fa//+W20tXcanNNkNLe0QalTMO3qpF+IOVtczc5DXpFwJs0XntwhUjZERERERERERDTdhoeHUVPXIIjNtuJGAAgLDRYsl1VUGe3YFy9fwZXM64KYmZkZnnh0Ozw83Ix2HrElJ8RhQbSwcWtndw8OfHkaarUaKpUKlzKE4zVB/j7w8/GczjQnzM/bAy88thWOOg35BgaH8P6Boygurx5jT6Kp4eHmjJS4GEGsur4JNwrKxtiDaG46f7Ma//NpGmpa9JumujnbY/fmpfj1D57E7s3L4O3ubJRzOjvYYuvqBLz26hP4xvY1CPDW/5xv7+rF3/aegKWFOV7evQmO9vqzN+8/fBRHT503Sk6TUdcgbIzr6mi6M0zfT3tnNw6dvCSIvbD7gVlzzxoRERHRVMrLy9OL+fr6TugYMTExiIqKwr59+4yV1n0dO3YMEokEmzZtmrZzGtPMriwgIiJRlOkWN87CmRutra3g4yUcUDLGYN7g4CA+2ntQr/gtwM8Xux5+cMYX9U2Uvb0dHn14C2QyYTezI8fOoKGxGalpmcLt7WyxYF7UdKY4aTFR4XjmsR2w1OlY2t7Rhbc+2IuGxmaRMqO5Kjl+ITzdhYME5y9loKenV6SMiGaWvoFhvLHvIm6WNuits7O1wUvPPoZXvvUc5sdETtnnsa+3J57YtQ0//eG3Eeivf0Gjpa0df33nEyxNWoRvP/+k3iyO7R2d+MPf3xNlBsdqnRu5nOxtYGdtOe15TJe3Pj4oWA7y98HqZYkiZUNERERERERERNOtqqZebzawkFk4JqpbsNnS2oZuI1x/vJZ1A2cvCG+Yl0qleHTHNvj7Texmr5lOIpFg64aV8PF0F8Qraupw8nw6cgpK0N0r7MK/InnRdKY4aa7Ojnjx8a3wcncVxBUKJfYcOY1rOUUiZUZz1cqkBXC0FzamPHnxKoaGh0XKiGjm0Gg0+CK9BAcuFOrN1iiRSLAuZT7+9Zu7sCI+BpYW5lOSg1wmQ/y8UPz4+Yexa0PKqDMfns3IxdW8Uvz0xZ1IXhCut/7LE2dx6vwlvfhUUygUaGgSzkzs7+Ew7XlMl48+P4WRkTvNgC3MzfDkw+tEzIiIiIjIdKSnp+s1hYiNjZ3wcX784x/jN7/5Derr6++/sRHs2bMHALB58+ZpOZ+xza4KCyIiMphGo0F5le7MjYHiJDPFdB9XWWXVpI+lVqtRX9+It97/BM0trYJ1Xp7uePyRh2FmZjbp489k/r7eeGD9KkFMqVLio/2H9X6XliXF6xVCzmQBfj54/sldesUn/QODeO/Tg0btbkt0P1KpFFs3rhH80aRUKVFQzG6lRN19g/jd3guoadaf9TB+YSx+8ZPvIS42etry8fJww4+/9yK2P7BBbxbHwaEhvPGPDyCXy/CT778EdzcXwfr2jk788R/vo69/YNryBaDXpd7f0zidXGeivv4B7Dl0TBB7/okds64JBRERERERERERja2sUjgrnZurCxwc7EXKZur4eHvBylLYxKysonJSx9JoNOjt7cOFi+n46vhpvfXbt27RmylytpDL5Xjs4U2ws7UWxK9k5eDE+TRBzM/bA4F+PtOZnkFsbazx3CNbEBooLErVaDT48kwazqZd1ysEJpoq5uZmeGB1iiA2ODSM8mr9xpZEc4lGo8H+C4U4cbVCb52HiwN+9Nw2bF+XDHMz+Sh7G59UIsHqpFj867d2IcRff6bi6/nl+ODIOezevBTbViforT/05UlczriuF59K9Y0tUKvV2mUJAF/32VncqNFo8PberwSxnZtXwtlx9n3XJSKir0mk/Hf7H5GBamtrUVtbK4j5+fnB1tZ2jD3GtmTJEqxfvx6vvPIK+vr67r+DAdLT01FQUICNGzdOKteZgK9gIiISaGxqweDgkCAWEugvUjZTS7e4sbWtHV06My6ORqFQoK6+AdeybuKLoyfx5rsf49e/fQOnzqWi++sZ1BQKBTQaDVycnfDU7l2wtJy9Mx8BwKIF85C4aIEgNjA4KFi2tbFB3PzpKywxFnc3V7zw1CPw0OlYOjKiwCcHvkB2Tr5ImdFc5OPtiajwEEGsq2f6Z3gjmkn6Bofxx/2X0NQunMXU1sYaLz37GJ5/6hHY2thMe15SqRTrVy/DT3/4bQTo3MiiUCjwt3c/RUdnF370nRfg4+UhWN/Y3Ir3PjkwrTeM6Bc3uoyxpenbd/iEoHhUKpXiucceEjEjIiIiIiIiIiKabroNLGfjrI3ArWtfIboNX8vvX9yo0WjQ3t6BvPxCnD6Xio/2HMBv//AXvP7GX/HW+x9BoVRCA2hnBNq8YS1i50VNwSOYOexsbbB72ybIdJqkDQ2PCJZXJC/W6+4/01lYmOPxh9ZjYUyY3rrUjJs4fPIiVCqVCJnRXBQW5At/b+G4SVfP1N4ESjSTaTQaHL5cgtScGr11a1Pm419e2okgX49R9px67s4O+MEzW7FrQwrkMuHnY35ZLd47dA7rlizAo5uW6u376cEvUF07PTPYAEBNnfBc7s62sDSfnU3qM28UIq9Y+H3vhd0PiJQNERERkWnZu3ev9v81Gg0kEglSUlLusce9vfHGG9BoNNi5c+eUFji++uqrkEgkeO2116bsHFNtelq1EBGRySirFF4Ms7KyhJenu0jZTC1vL09YW1kJivDKKqoQf1eR3sDAABqbWtDU3IKmllY0Nbegrb1j1GKDmrp6uDg5If3qNTQ0NiM5YRGefmwXbGys9badjTauWY6W1nZU1daNun5J4iKTnb3S3t4O33hiF/Ye+hKV1Xcen1qtxuGjp9HT24cVSxJNbqCSTJOzo6NgubevX5xEiGaAoREF/nLwMhrbhUW+zo4OeOVbz+nNiigGLw83/PA7z+PtD/cht6BYG1coFPjLOx/jh99+Hq9881m8/pe30dLarl1fUFyG9KvZWJK4aMpzVCqVqGtoEsT8ZvHMjW9/fFCwvGXtcr0CUyIiIiIiIiIimt10Z27UbYo6m4QGByKvoEi7XFFVA5VKBZlMBuDW9cGW1rZb46HNLWhsakFzSytGRkZGPV5QgD96e/tQUlYBqUyKf/r+y0hKmPrrmDOBr7cHtm5Yic+Pnxt1vbeHG0KDTLNxsEwmw0MbVsDe1gapGTcE624UlKFvYBCPPrAG5rO0EIRmFicHO9Q0NGuXe+9qWEg015y6XonTWcJCNQmAxx9YjqWLxG8scHsWRy83J/x930mMKJTadTeLq/DxFxfw9LZVUCiVOHQ6Q7tOrVbjgz2H8C8/fBlm8qm/jbmqRljc6O/uOOXnFMtbOrM2hgR4Y2XSgjG2JiIiIqK77du3T3sv+O3/dnd34+233570MefNm4d9+/Zh7dq1OHjwIHx9fe+7T2FhIfbs2YPu7m4sXboUjzzyyJjbPv/88+jp6cF//ud/muysjcCMKW6UfP3PmMebiwx53NM3IwiR6THh18ckptguqxIWN4YEBkD69cDWBE484fMajWb8HSOlEiAk0E9QaHAjJw+9fX1obGpGU3Mrenp773EEoYqqapxLvYyRkRGoVGp4urvD0dFhQulPmkG/psb5HZfJZHj04S34xwd70NUtLDKxtrJC/MJ5RjmPsUx0NiwLC3M8+chD+PyrU8gtLBasO3sxHd09vXhgw2pIpZwYm6aWna1wBrqxixvF/Pwy4NyGzFSnUU9+X4OJ+Xwb8Lk7ie8KRqMaNmh3jWIIe07cRHVTpyDu7uqCV15+Ds7T9Rk8DuZmZvjms4/h/U8/w7Ubudr40NAw/vbuJ/j5j76LV775LH7zxze1s0ADwIEjxxAZHjLljyU7twAKhUIQ8/dwBNR3vldJpBP9PngX6Qy59AAgv6gM6dduCmIvPLlj7B0Mmj1TrH0NZeB3eUOeMzEva7BJxTQz4b+zTTp3IiIimpkM/S5qqt9PxPoObuDzZcjfDgb9jWnKTHQMW6yftaF/n87F37NJXuMsqxCOiYYGB03z9YHp+1ndKty8c77h4WGcOnsBg4NDaGpuQWtbO9Tq8V1fHxwaQk7ereuJHm5uAAA/H+8pyHoUol6+ufP8LZwXgaaWNlzJytHbakXyoq9/je7++ZrO61IikWDN0njY29rgq7NpgvHUsqp6vLv/KJ58eD1sx9Pc16D3UQPHLiQGjBcZ8D5qaCNcDSb/uEVtwjsF57azsRIs9/YNjr7hnPxuZsh4qIGvLTG/4xj0szbkvAY+ZyrF/bcZi3oERbWdOJJWIkxJIsE3tq/B4pgQw3IzsshgX3z/qQfw54+PYmjkzuPOyCmFv7cb1qUsQG//IE6n3/n8bGxuwbFT57Ft87opzW1oeBg38goEMX93W0B1VyMHQ+81MGQ8dJzfw0Y/r3Cxt28Ae744I4g9/8hmSCWaCd1TNz5ifjkz4LUpMfD935DfFYkBY+6GEmsMFzDwfZhjuNOLzzcRzW0FBQXo6enRix8/fhzHjx83+Pjd3d3Yu3cvfvSjH903j507d2qXT5w4gd7eXjz//PN62/7gBz9AWloaXnrppXsWQJqCmXOHIRERzQj6A3kBAICh4RHUNbagubUdwyMKKJRKqJQqyOVyyOUyWFiYw8vdBb6e7ibVrTE0OFBQ3FjX0Ii6hsZJHcvczAzm5mZwdXFGgL8vOrq6UF1TiwB/P2OlO+NZW1vh8R1b8daH+6BQ3rlYmRy/EObm5iJmZhwymQw7tm6EvZ0tLmdeF6y7fjMPvX392LVt06x4rDRzjb+4kWh2u1nejKvFws9sJ0cHvPKtZ2dUYeNtMpkMzz6+AyMKBXLy73RJ7+zqxmdfnsCTjzyEJ3Ztw1/f+Vi7bmhoGJ/sP4zvvvj0lN6YkJqWKVgO9XOHnbXllJ1PTG9/8plg2dPdFVvWLRcpGyIiIiIiIiIiEkt5lf7MjQqlEvWNLaitb0Rndw8UChWUSiXUGg3M5HKYmclga20NXx9P+Ht7wsrKNK6h2drawMvTA41Nd2ZBu6IzzjVelhYWkEllMLc2g4eHG+xsbXEx7QpCQ4KMla5J2LAqBS1tHaioqdPG3F2dEREaKF5SRhS/IAp2ttY48NVZKO6agauxpR1v7fkST23fAFdnR/ESpFnPTqeAljM30lw0NKLEx+dK9OJPbV054wobbwv29cDLj23Cnz85CoXyTiHd4TOZmBfqj22rE1FUUY+65nbtupPnLmHBvCgE+PlMWV7XsnMxNHSn8a4EwPwQjyk7n5j2fXUe/QND2mWZTIpnd24QMSMiIiIi0xEdHY2ioqL7bzjF9uzZA41GA4lEom089fe//11Q3FhYWIhXX30VNTU1ePHFF+9bMGkKWNxIREQC5ZXCgbyhERX+43dvo7mtfVwNfKRSCTzdXeHv7QF/H08E+XnB38dT3E6B93CrU+nESSQSuLo4w9PDHZ7ubvD0cIeriwve/uAT9PT2abe7mJYxp4obAcDD3RWPPLQZh746icGhIQT4+iA5fqHYaRmNRCLB+tXLYG9ni+NnUwUdS0vKK/H+ns/wxM5tsBlPx1KiSbCz0ylu7O3X/iFDNFf0DY5gz7l8QczKyhKvfPNZODs5ipPUOMhkMrzw1CP44z8+QNld37kuZ1xH3PxoxEZHIDl+Ia5cu6FdV1BchrTMLCxNWjwlOdU3NqO8UtjcYkVc2JScS2zDwyP4cP8Xgtizux+CmZnpNOYgIiIiIiIiIiLDKZVKVFbXCWKXr+bgys0iqFTjn4nFzcUJAb5e8PPxQkRIIPxm+Jjo3cWN42VrawNPD3d4eXjA0+PWmGhVTS2+OHpSu011Td2ca/gqlUrxyNb12HP4OKrrGmFlaYntm9fM2J//ZESEBODZnZvwyeFTGBi8UxDS1dOHd/Z9hce3rYOf9+wsDCHx2dmyuJHoUFolOvuGBbGH1yYieUG4SBmNT1iAF17YuQ5/23tCGxtRKPHRFxfwytMP4qltK/G/bx+CWn3rXhu1Wo0P9hzCv/zwZZjJjX87s0ajwYXLGYJYTJA7XOxn5z09b+87Klh+YHUyvD1cRcqGiIiIiAx1d4EjcKuo8e9//ztOnDgBOzs7vPvuu0hJSRExQ+NhcSMREWn1DQwiO0/YcWBgWIGm1vYx9tCnVmvQ0NSKhqZWXMnKAwB4uDljRVIckhfNg5WlhVFzNpSNjTX8fLxQWz/2bI1yuRwebm7w9HCDl+etgTt3N9dRZueTYGlyIo6dOquNlFVUob6hET7eXlP0CGam8NAgfP+lZzAwOAgnRwfIZDKxUzK6pPiFsLO1wWdfnoRSdadjaX1jM97+aD+eevShGV1gQ6bLztZWsKxUKTE0NGwyHaKJjGH/2Rz0DowIYo8+vAUe7jN/YMbMzAzPPr4Dr73+ZwwP33kMH+07jF/8+HvYtW0zCkvK0d3Tq133+VcnkRAXOyUzA+vO2mhnY4kF4b5GP89o2ju7cfFqDlIzc1BeVQ+15taNYxJItDcBSSQSQGYGCW4v40786/8KthX89+ujfb1Ne2cX2ju6BDk8/8T2KXyEREREREREREQ006jVGpy+eAVKpVIQHxxRwEo2sVtoWts70dreiWs3CwAA/j5eWJGyGPELYmBuPrMaakWGh+JiWsY9t3F2coKXp/ut5q4e7vDy9ICtrY3edo6ODrhwKR09d13DTL18BU/PoeJG4FbDved2P4T2ji7Y29nOuJ+5Mfh6ueOF3Q/io0Mn0dl95+c9MDiM9w8ex67NqxAZGiBihjRb6c3c2DfAZq80pxTVtuNygfA+prAAL6xNWSBSRhMTGx6AZYujcOl6oTZWWt2Ii9cKsDIhBpuWLcLR1DuzSDc2tyAtIwsrlyYaPZeKqhrUNwobPKyY72/084ymu3cAl7KKcPF6AW4UVWtnQ5ZIdMY1pfI7yxhtzFMy+hgpJMBdx1Kr1biSfec5B4AXdm+e0sdIREQzxK0PF7GzEB+fA5olNm/ejH379mmXJRIJenp6kJSUhO7ubtjb2+NHP/oRXnzxRRGzND4WNxIRzXEajQbV9U1IzbiJazlFqKmtF6x3cHAw+BzNrR3Y/+UZHDmZioQF0ViRHAdfL3eDj2ssG9euxJ7PjqCvbwBWlpbaAsbbXUhdXJwglUrHdaxFC2ORevkK+gfudA5MvXwFjz8y926ct7a2grW1ldhpTKnoyDDY2trg04NfYHBoSBvv6OrC2x/txxM7t8LH21PEDGk2sh1lVtDevn4WN9KccbO0AdeKhF3V50WFI3GRaQzmAYCLsxO2P7ARez67M4tgV3cPDn5xHE89+jCe2LUNf33nY+26/oFBXL+Rh5TERUbNo39gAJlZNwWxpfNDIJ+ipgRNre1IzchBauZNpGbeRF5x5ZScZ7xWLUlAWDBvvCEiIiIiIiIimgv6BgaRfj0XFzNuIPtmrmCdubk5LC0Nv8ZeU9+Ijw58iYNfnUbK4gVYnrwIHm4uBh/XGHy8vbA0OQFpGdcglcrg7ub6dQHjrUJGD3c3WFiMr0mtTCbDspREHD1xRhsrn6MNXyUSCVxdnMROY0q5ODnghd0P4JPDp9HQ3KaNK5Uq7P3yLLasTkbCgigRM6TZyM5WeJ+BUqXC0PDIjGumTTQVhkaU+PissEDN3EyOp7auhNSEbtjfvjYJBWW16Oju08Y+P5OBmFA/bFoWh5ziKtQ132m2n5qWiRVLEoxexHzu4hXBsquDFSIDpqZhbltnDy5ev1XMmHqtEDeKqgSz7Ew3L3cXbF6ZJNr5iYiIiGhyUlJS8Ktf/Qp79+5FXV0dHBwcYGdnhyVLlmDp0qWzZqZGXSxuJCKaw1raOvHJ4VMorbxVIDA4OIiREeEsSPb29tr/l0ql8HR3hZ2tDczMzCCTyaBUKqFUKtHd04fm1rZ7XpQZHlHg0tWbuHT1JmLCg/H4wxvg7Gg/5vbTxdfHCz/8zosYGVHA0sbWoAtlZmZmWJIUj1PnUrWxkrIKjIyMTMlsSyQ+f19vPP/kLnx84Ai6unu08f6BAbz36Wd45KHNCA8NEjFDmm3kcjmsrawwMDiojfX29cF9htwgQTSV1GoNDl7IE8SsrCzxxK5tJtetd3lKPLJz81FcWqGNpWVmYdWyZMRGRyA6IhQFxWXadRfSMo1e3Lj/82OC2SMlEgmWLQw12vFrG1pwITMXqdcKkJpxEyWVtUY7tjG88OQOsVMgIiIiIiIiIqIpplarcS7tOr44dQkjCgUAoLu7W7CNg4OD4Pqiu5sLvD09YGlhDjOzW7PxKZVKjCgUaGltR0NTC1Qq1ZjnHBwcwtlLGTh7KQPLEuOw/YF1M6IgZ/2alVi7ajk0kEBmYIOzuAW3Gr729fVrYwVFJXOuuHGusLWxxnO7NmP/0XPaewuAW42Uvzqbjp6+fqxZstjkrtPTzGU7ShPl3r6BGfFeSjTVzufUorN3SBB7eG0SXJ3Ev79qIiwtzPHk1pX440dfaWMjCiW+PH8Nz21fg4fXJeFPHx/VrmtsbkFZRTXCQgKNlkNeYQmycvIFsWWx/kYrEm1s7UTq9WJczCpC6vVi5JfNrLHQ53ZthFw+NU1tiYiIiGhq7d69G7t37xY7jWnF4kYiojlIrVbjfHo2jpy6BIXyzsCb7kCeVCrF+tXLEejvC39fb/h4e8L86wG80QwPj6CuoRHVdQ2oqWtARVUN2to7R902v6QCr/3hHezYshpL4+eLPtAhk8lgZSUzyrTkurOnyeUygwcIaWZzc3XBC08+go8PHEFTS6s2rlAqsOfQl3hww2osWjBPxAxptrGztdEpbuy/x9ZEs0d+ZTPauwcEsUcf3gJHB9MazANuFRI+9chDeO31PwsKDFPTMvHErm1YsSRRUNxYU9eAqpp6BPr7GOX8OflFerM2xkX4wcneZlLH02g0qKhpROrVXKRm5uJCZg6q6pqNkeqUWDgvEjsfXC92GkRERERERERENIVa2jrw4cFjKK+uF8R7enoEy4EB/tjx4Eb4+3rDz8dLb6xPl0KpRGNTC2q+HhMtKCpFR1f3qNteysxGQUk5ntz5IKLCgw17QEYglUoBidTg48jlcr3xXSsjzH5JM5e5uRke27oWX55JQ3Z+qWDdxcwc9PQOYNv6pRwXJ6OQy+WwtrTEwNCdAq/e/gG4u87umVKJVGo1LuXVCWJhAV5YHh8tUkaGiQzywbLFUbh0/c5MlNmFFdi5IQURQT5wd3ZAS8ed71Cp6ZlGK24cGBzEJweOCGKW5jIkR/tO+pg1jW24cK3oVjHjtSKU1jQZmuaU8fZwwSvPbhc7DSIiIiKicWNxIxHRHNPc1omPPjuBipoGvXW6xY1hIcF49vGd4z62hYU5QoICEBIUAODWTe4lZZVITcvEzfwiqNVqwfZDwyP45NAJZOUW46kdm2bELI7GkF9YLFgOCwnmIM4cYGdni288sRP7Pj+K8qoabVytVuPI8TPo7unFqmXJohfy0uxgZ2uD5tY27TKLG2muSL1ZKVj28/FC4qIFImVjOBdnJ6xdsQRHT53Xxq5m5WD7AxswLyoczo4OgpuiUtMyEOhv+GyD/QMDeoN5VhZm2Llm/DNDajQaFJXXfl3MmIPUzDzUN7fdf0cdVpYWWLI4BslxMbC1sdLOAq7R4M7/yyy157z9Xw3ubKe7TruNRgNIJIJjAoCvtwce2boBluzwTEREREREREQ0K92erfHIyYtQKJV663XHRDesWYF1q5aO+/hmcjn8fb3h7+utPV9+USlS0zJRUFymvR51W0dXD/749iczahZHQ9XW1aO3t08QiwwPFSkbmi4ymQzb1i+Dg50tzl/JFqy7WViG3v4B7H5wDSwszEXKkGYTO1trveJGotkur6oNXX3Dgtj2dclGm2lQDFtXJeDKjRIov571WqlSI/1GMTYsXYjli6Nw8NQV7bbZOQXo7umFg72dwec9eOQ4urqFDS0eXhYJW6vxfUZpNBqU1zbfKWa8XoTqhomPhVqYmyFpfiiWL46Ch4vjKGOagEZupRO7e0wUY64DAA0kejEnBztsW5cCDzfnCedLRERERCQWFjcSEc0hqRk38dmx84LZGm9zdLCHp7uLIBYaEmTQ+SQSCSLCghERFoyu7h5cunINl65cQ4/OQFdRWRVe+8M72L1tPZLiYgw6p9gGBgZQWV0riEVHhouUDU03CwsLPLFrG44cO4Ob+YWCdRfSMtHT24cHN65hsSsZzM5WOLMaixtpLmjt6kdhpXAmwJVLk0y+aHxp0mIcP5OqbQIxPDKCjOs3sWpZEpalJODIsdPaba/dyMPObZtgY21t0Dn3f35M7/vYzrWL4Wg39nHVajVyiyuRejUPqRk5SL2ai9aO0bvR34udrTWWxcdieeJ8rExaiMXzwmFuPvbM4AAASwM6MUsN70JPRERERERERESmo6e3H299ehhlVXWjro+Pi8WpkycEsZCgQIPOKZVKERsdgdjoCLS2deDilau4mHYVwyMjgu1uz+L44lM7EejnY9A5xabb7NXdzRWuri5jbE2ziUQiwaqUONjbWePLM2lQq+8U81bUNODd/Ufx5MMbYGdj+kW8JC47Gyvc3VOxt29QvGSIpsnFXOH3lwBvNwR4u4mUjXHYWlticUwIMnJKtLGL1wuwLmU+khdE4Mi5q9r72NRqNdIys7B53UqDzplXWIL0q8Ii/Eh/Fyyd5zfmPhqNBoUVDbhwvRAXrxfh4vViNLR2Tvjc1lYWSFkQjpUJ0Vi+OAqJsaGwvE/Rv4RjoURERERELG4kIpoLNBoNvjydhuMXMkZdv2pZMh7avA4vfPtVQdzQgby7OTrY48GNa7BmRQoOfnEC6ZlZgvVDwyN4f/9X6O3rx7rliUY773QrLi0XzFApl8sQHhosYkY03WQyGR5+YD3s7Wxx8cpVwbrs3AL09Q/gkYc2w9ycHUtp8uxsbQXLLG6kueDSzUrc3e/cysoS8QvniZaPsTg5OmB+TCRu5BZoY6lpmVi5NBFLExfhq5PnoLrdyVSpRGl5FRbGRk/6fKlpmcjMuimIzQvxRvI8YVMLpVKJ7PxSpGbeRGrGDVzMvImuHmFB5Hg4OdhiecICrEhagBWJ87EwOhRyOS9FEBERERERERGR8bV3duOP7+xDS7v+jeiuLk546tGHERoUgO98/4eCdYY2fL2bm6szdjy4ESuXJOKjfZ+juKxSsL6jqwd/+MdH+NazjyIy1HjnnU4ajQaFxaWCGJu9zj2L5kXA1toK+4+eh0JxZ4bUptYOvLXnCzz18Dq4uTiKlyCZPDsbYUNGztxIs11zZz+KajsEsRXxpt0g/rYV8dGC4saO7j7kl9UiNjwACfNCkXbjTtOEotJyg4obm1vb8OHeQ4KYpbkMT6yNFTTNValUyC2pxsWr+bhwNQ8Xr+WhrbN3wuezt7XC0rhwrEyYh+WLo7A4OhhmZhwLJSKiaSaRABIWvEOivv82RDRj8Vs0EdEsp9FosP+rc7hw5YbeOlcXJzz96HaEhQQCAMoqhINrxhzIu83aygpPP/owFsVG4+MDR9DV3SNY/9mx8xgcGsaD65aZ5ExMBUUlguWwkGAWsc1BEokEa1cugb2dLY6ePg+N5k45TmlFFd779DM8sXMrbHVm3yMaL/2ZGydecERkShRKFdLzagSxlIS4WfMZu2JJgqC4samlFaUVVQgPCUJokL/gBqiauoZJFzdmZt3E3kNfCWJWFmZ4fGMiFAolruUW4cKVW4WMl6/nordv4jcKuLs4YkVCLFYk3vo3LzwQUjOrSeVLREREREREREQ0Xi1tHfj923vR1a1/U/qqZUl4aPN6WFiYo7auHsPDw4L1ocHGHxN1cXbC97/5LC5duYZDX54UzOI4PKLAX97Zgxef2on50aZXFFhX34CeHuHzHBMVIVI2JKbwYH88t2szPjl8Cv0DQ9p4d28/3tl/FI9vXQt/Hw8RMyRTZm+nMx7K4kaa5dIKGgTLNlYWWBQ9O5qpB3i7wd/LFTWNd6ZjvZRViNjwAMSGBwiKG2vrG6FWqyGdxIyEHZ1deOPv7+s1h354WSTsrc2RmVOiLWa8nFWArp6JN5F2drDF8kURWLE4EiviI7EgPAAymRSQzY5xayIiIiIisbC4kYhoFtNoNPjs2IVRCxvvHsS7Ta+4cQoG8m6LiQrHz3/8XRw8chzpV7MF646dS4dcLsfm1SlTdv6pMDg4iIqqakGMXUrntoRF82Fna4MDR45DqbrTsbShqRlvf7wfTz3yEFycnUTMkEyVbnFjHwfzaJarbe5C/9CIILY8JUGkbIwvIjQYHm6uaG69M6BXWFyG8JAg+Pl66xU3TkbG9Zv4cO8hbcG9UqlEc3Mz3O1k2PWtnyI9Kx+DQ8P3OYo+Hw9XrEyKxYrE+ViRMA8RwX4m2aCCiIiIiIiIiIhMV0dXD954Z59eYaOzowOeeXwHwu9q6FpWLhwPtba2hqfn1BRfSaVSrFiSiJjIMHyw5xBKK6q065QqFd766CC+8/xjJjeDo26zVzdXF7i5uYqUDYnNx9MNL+x+EB8dOomOrjuNjQeHhvHBZyewc/NKRIUGiJghmSo7G2HjxMk0ZCQyJcW17YLl5AURMJ8lMwBKJBIsj4/Gx1+kamMlVQ1Qq9Xw93ITbDs0NIzW9g54TPC7RVt7J/74j/fR2dUN4NbMjC0tLVAOduO1nHSkZRcJCvHHy8PF4VYh49f/okN8JlV4SURERERE9zY7/vohmnM0999kJtKImbeJPmcGOn4+A2fTsgQxiUSCJ3Ztw9KkxYJ4Z2cX2ts7BLGpmLnxbtZWVnh693Z4urvh0FcnBeu+OHURVpYWWJWyaIJHFe9m+uLSMqhUKu2yXC5HeGgQZvzvn8GvTUP2n/3FD5HhIXj28e349OCXGBgc1MY7u7rx9kf78cTOrfD18RIxQzJF+jM39kOj0RixoGiGv2+NxdDHb8j7ocTQAQxDnnO1AfsamLfccvL7alT33+ZrNW3CrpleHu4THtCaySQSCRbMi8TJc5e0sdtFjP6+3oJta+oaJ/x6v3A5A58cOIKGhgbtv5aWFqjVE//dCfb3xoqkhViRHIcViQsR5O99/1xkZhM+j4Ahr21Rv+eISMznbE4Wtxr6eyLm77gB5uTPmoiIiMhQEhj/mii/l02Moc+XIdePTPS7v6gM+Xnx7yWTYsBz1ts3iD++s09QVAXcuob4/W8+A0cHe0G8vFK/2etUN+u6NYvjM3jvk4PIysnXxpUqFf7+/j688tJTCPL3mdIcjEWj0egVN5pMs1fR3ksN/P0yZPxhmh6zs6M9Xtj9AD45fAr1TXea+ClVKuz76hw2r0xC4sKoacnFYAZ9Xhs25iKRGDLeIyJDfs/usa+djZXg47y3b2DmfCeaq5/1EpmI5zbg9WHAa1MiNez2Ws04x1IVShUaOoTjobHh/gade6aJDRMWuo8olGhq64KXmxPsbKzQ23/nfpqauoYJjQU3NrXgD39/D0XFJaivr0dDQwOam5sF93CNl5+nC1YkRGP54misTIhGWIDX+L4ryiwmfC6jMPh9ceLP0Z1zG3ivgSHvpXPxbVhj4PcEMd/DaZrNxRcIERHNBixuJCKapXKKKvDlmTRBTCqV4htP7MLihfP0ti+vrBIsy2QyBPj7TWWKWutXL4OVlSU+OXBEEN//5Wn4eLohLGh68jCU7kBeaHAgLCxEunhFM4qfjzeef3IXPtp/GF3ddwbXBwYH8f6eQ9i1bRMiwoJFzJBMjW5xo0qlwuDgEKytrcbYg8i01TQJGzAE+HmPsaXpGquIMcBXeFNTX38/Oru64ezkeN9j9vX3Y9+hozh4+Eukpqair69vwnlFhgRgedICrExehOWJC+DnPTVd7ImIiIiIiIiIiCZKo9HggwNfoblNeP3Q39cb33vpGdjaWOvtoztzY0hw4FSmqCWXy/H8U4/AYr850q9ma+PDIwr848MD+MUPv2kS1/jrGxrR3S0sJI2OjBApG5pJbKyt8OzOzThw7DxKKmq1cY1Gg6Pnr6C7tx/rli2e8mJimj3sdN7De/sHjNzslWjmaGjrhVotLFLz9Zw9jV6BWwXLjvY26Oq5U8RZ29gGb3dn+Hu5Ir/szmdHTV0DEuLm3/eYarUaF9Iy8fb7n+L0mTNobm6ecF4hfp5YHh+NFfFRWBEfhUAfd77PEBERERGJgMWNRESzUP/AED49ckYv/vTu7aMWNgJAeYVwIM/fzxfm5uZTkt9oliXHQ6FUYv/nR7UxjQb48OAx/OyV52AxjblMxtDQEMorqwUxk+lSStPC1cUZLz71KD4+cASNzS3auEKpwJ5DX+KB9asRHxcrYoZkSmxsrCGRSKC5qwtfb1+/Sdz4QDQZtU2dgmU/39lf3Ng/MICOzm64ujjBysoSg4ND2nU19Y33LW7Mzi3AW+9/ihMnT6G8vHzcecyPCsHyxIVYmbQQyxMXwMPN+dYKg2cnJSIiIiIiIiIiMq70rHzkl1QIYl4ebvjeS0+PWtgIAGUV+jM3ThepVIonH3kIwyMKZN3M08a7e3px4MtTeObRbdOWy2TpNnt1dXGGu/vsKr6gyTM3N8NjW9fiq7PpuJ5bKFh3+Xouevr68fCGZZDJOHMQ3Z9ucaNarcHA4BBsOB5Ks1BNc5dg2cPFAVYWM/s+qckI8HITFDfWNLUhaUE4/L3cBMWNtXWN9z1Wa1sH3vl4P/Yd+Aw3btyAWj2+We2ign2/Lma89c/Hw+XrNSY6ey4RERFwazZcFufzOSAycSxuJCKahfYfu4CevgFBbNdDm5G0eMGY+4g5kHfb6mXJ6O8fwNFT57Wxto4uHD5xEY9uXTvt+UxEcWk5VCqVdlkmkyE8lDPxkZCtrQ2ee3wH9h8+hrK7imE1Gg2+PHkWPb19WL08mV3g6L5kMhlsrK3R13/nwn9vXx88eAMBzUIjCiUa24WdwHULAWcDF2cnWFtZYWBwUBurqW+Ai7MjvDzcUFF1Z0Cvu7t3zOP09vVj72df4qM9+5Geno6RkZExt5VKpVg0LxzLExdgZXIcliXMh7OjvXEeEBERERERERER0RTq7O7FwaPnBTEHezt8/5vPwtbGZsz9dGduDA2Z3jFRqVSK5x7fgb6+fpTclcuV6zmIi41CbFTYtOYzERqNRq+4MToyguNaJCCVSvHg2iWwt7HEuSvZgnW5xRXoGxjE7gdXw9LCQqQMyVTYWFvqN3vtH2RxI81KNS3dgmU/LzeRMplafl6uuFlcpV2uaWwFAHi6Ogq26+3rG/MYt2dr/OPf3sHZs2fR3d095rYSiQSxYf5YkRCD5YtvFTO6uzgY9BiIiIiIiGhqsLiRiGiWySmqwNWbRYJYTGQYVi9Lvud+Yg/k3bZl/SoUlZYLbuA/n34dcfPCERbkJ0pO46E7kBcaHAhLS0uRsqGZzMLCAo/v3Iovjp/FjbwCwbrU9Ez09PZh66Y17FhK92Vna6NT3Nh/j62JTFd9S5dg4FoikcDP21PEjKaGRCKBn68XikvvdJqvqWtAXGy03mzaSqVCb/+augZcuJyBE2cu4MyZM2hqahr1PHEx4diwMhErEhdgafx82NuNfaMXERERERERERHRTKTRaPDJ56cwNCxs7PXErm1wdNBv3qXRaNDe0YXq2noUl5YL1jW2tOGL42dgaWEBb093+Pt6w87Odkrzl8vleObxHXjtt3/C0NCwNv7JZ0fxix9+E9YztHCnobEJXV3CAoLoqHCRsqGZTCKRYGXyQtjb2eCLM2mC2bQqaxvx7v7jePKhdbw+TfcklUpha2OF3rsae/f2DcDTzVnErIimRl2rTqNXr9nZ1Fj3cdU1tUOt0cDcTHgb84hCqbdvT28f0jKv4+SZVBw9fgLFxcWjniPQxx071idj+eJoLFsUCWdHO+M9ACIiIiIimjIsbiQimkX6B4bw6ZEzgpiVpSWefOSh+3bMnAkzNwK3LlA/vXs7fv36X6BQ3rlY9eHBY/jZK8/BQufm/plgeHgYZRVVglh0JAfyaGwymQwPbVkHB3tbXEjLFKy7kVeA3r4+PPrwFliwYyndg52tDRqb7yyzuJFmq+7+IcGyi7OjXrHfbOHl4SYobuzpvdWV1NzMTLDd7e9IIyMjyLqZj9S0TJRVViMrKwvZ2dmCG0Vuc3a0x+u/+D6e2bmJndSJiIiIiIiIiMikXcnKR0FplSCWtHghYqMjAADDwyMoKClDdW09amrrUVvfiP6BQQwMDGBoSHi9sayyFs1tXYKYk6MD/H294e/rhaAAP4SHBEEqlRr1MTg7OmDn1k34eP9hbay7pxcHvjyFZx7dZtRzGYtus1cXZyd4uM/OmaXIOOJiwmBnY4V9X53HiOJO077mtg68ve8onnxoHdxdnUTMkGY6O93ixv6Be2xNZLp6B4YFyx4ujuIkMsV0H9eIQokRhRJmcuFtzIqvPzPUajUqqmuRmpaJrJv5KCwsRFpamt73OQCQy2T45xcexr9+ayesLHmvDRERERGRqWFxIxHRLHLi4lX09Akv5u56aPOoHUp1zZSZGwHAw80V2zavxcEvTmhjbR1dOJd2HZtWpYiW11iKS8uhUqm0yzKZDBFhISJmRKZAIpFg9fIU2Nna4qtT5wSzkpVX1eC9Tz/Dk7u2wdaWHUtpdHY6vxssbqTZ6u5mBwBmbWEjoP/YlKN0JQWA7JwCZN3MR0NTC9RqNRoaGnDhwgV0dXWNuv1TOzbh9V+8Ajfn+38nJCIiIiIiIiIimskUSiUOn7wkiDnY2+GRhzajqaUVqWlXkXHtBgZHuem9u1s466BUKoWNjf44TGdXNzq7unEzrxAA4OzkiGXJ8ViauMioszouSVyE7Jx8FBSXaWNXrudgzbIk+Hp7GO08xqDRaFBQKCxujI6MYCM1uq/QQF88t2sTPv78NPoHB7Xx7t4+vLP/KB7fuhYBvp4iZkgzmZ2NNYB27TKLG2m2UihVgmXdmQxni9Eel1LnsQPA0PAwfveXd1Bb34ih4WF0d3cjNTUVdXV1ox43aUEE/vGf38O8EG+j50xERERERNPDuK3liIhINCMjCqRn5QtiMZFhSI5feN99+/v70dTcIoiFBAUaMbuJW708BcGBfoLYxYwbUKn0ZyISm26X0uDAAFhaWoqUDZma+LhYPLb9QZjJhbNyNTa34K2P9qGtvUOkzGim0y1uvD3DG9Fso1AKP/vN5bNzMA/Qf2y3CzsVd3W0BoDa+kbUNTRhYGAA586dw+HDh0ctbAz088KJj/6AD37/b3BzYfdrIiIiIiIiIiIyfTfyS/WKW5LjF+LND/bgP/73jzh/6cqohY2AfnGjvb39uGZk7OjswpFjp/Gz117Hu58cQHlVjaBp5WRJJBI88chDsNSZXSj1ynWDj21sjU3N6NS5BhkdFS5OMmRyvD1c8eJjD8DF0UEQHxoewQefnUR+SeUYe9Jcd6u48Q7dZt9Es4VC514oM7lMpEymlu4MjQCgUCgxotPsdmREgdKKKvQPDCArKwt79+4dtbDR1toKf/q3l3Hp0//BvPCAKcubiIiIiIimHosbiYhmiWt5JRgYHBbEHnl4y7i6ZZZXVOnFgoPEvegjlUrxyENbBLHO7l7kFpeLlNHohoeHUabz/MVwII8mKCIsGM8+th3WVlaCeFd3D97+aD9q6xtEyoxmMgd7O8FyXUMT1OqZVwBOZDAj3CRkqm7fIDWiU9yo0WhQWlqKPXv2oKioSG8/mUyK//edp5F3+hOsX5E4LbkSERERERERERFNh9SMm4JlM7kcJ85eRHHZ/YujBgeFRTFBAf5IWrwQi+bHIDjQD+ZmZmPseYtSpcLVrBy8/qe38Ie/vWeUBpXOjg5YszxFELuanYvBwdELNMWi2+zVydERnh7uImVDpsjJwQ4v7N4CX083QVylVuHAsQu4kp0/xp40lznYCZu9Vtc3i5QJ0dTSvbNrto6OakZ7ZBJgZESpF25ubsaBAweQkZEBlUp/dsft61NQcOwv+PbjW8bVrIKIiGhWk0j57/Y/IjJZs3fKCyKiOUSj0SA1I0cQi44Ihbury7j2Ly2vECz7eHvBSqfISgwBfj4I9PNBVW29NpZ6JRsLo8NEzEqotLwSyrs6iEmlUkSEhYiYEZkqXx8vvPDUI/ho/2F0dt3pHDw4NIT3Pz2EnVs3IioiVMQMaaYJ9PcVLPcPDKC+sQl+Pt4iZUQ0NczMhJ1JdTt3zia6j83czAwajQbNLW3aWE9PD1JTU1FbWzvqMRIXRuPv/9+/YMEM+r5ERERERERERERkDHWNraioETaEVIxxvVAikSA0OADBAX7w9/WGv683vvuDf8aVu7ZZsTQZzz6+Q7usVqvR1NKGmtp61NQ3oLS8CvWNoxfSlJRX4rXf/hkPP7ABK5YkGHRT/bLkeBw/k6ptYDg8okBGdi5WLUmY9DGNSaPRoKBQWNwYEx0xria7RHeztrLEszs34eDxCygqr9HGNRoNjl/IRE/vANYvj9cr8qG5K8jPS7Dc1tGN9s5uuDg5jLEHkWkyk8swrLhTwKdU6hfzzQaKUR6XmVyOprZO7fLIyAgyMjKQl5c36jF8PFzwx19+Cw+tTZ6yPImIiIiIaPqxuJGIaBaorm9GbWOLILZiyfhn6SkuLRMsR4TNnAKqFUsSUbX3kHa5qKwKzW0d8HB1FjGrO/ILiwXLwYEBM6IwlEyTi7MTXnjyEXxy8As0NN0ZLFeqlNh3+Cg2r12JxMULRMyQZhInRwe4u7qgpa1dGysurWRxI806ZnLhn60jIyMiZTL1RkaEMzTKzeTo6OxGX/8A1Go1cnJycPXqVUFjhdtsbazxX//8Mr7zzA7IZDK99URERERERERERKbuYubN+25jY22NpUmLsCw5Aa4uToJ1emOi4cIxUalUCm9Pd3h7uiM5IQ4ajQbVtfVITcvEtRt5etflRhQK7Pv8K2Tn5OPp3Q/D1WVy45eODvZYEBOJ7NwCbSw1/TpWpsTPiALC5pZWdHR2CmLRkeEiZUOmzsxMjkcfWI1j5zNwNadIsC4tKw89ff14eP0yyOW8zk2Aj6crbG2s0Nc/qI2VVNYhhcWNNMuYyYVNEoZ1xgxni9Eel5lchprGW41eKysrcfHiRfT39+ttJ5FI8N0nH8B//uAp2NtaT3muREREREQ0vTj3KhHRLHDxaq5g2dnRAfOixj+gVFR874E8MS1aOA821sJiwUuZN8RJRsfIyAhKyysFsZgJPO9Eo7G1tcFzj+9AWHCgIK7RaHD09HmcPn8ZGo1GnORoxokIDRYsF5dVjLElkemyt7EULHd0dmNEMTsH9JpaWgXL9rY2qK6rR3NzMw4ePIj09PRRCxu3rV+O/DOf4PvfeISFjURERERERERENCsNDA4h7froM/gAgL+vN559fCd+/Ysf4eEHNugVNqrVahSXlgti92v4KpFIEOjvi2ce24Ff/+LH2P7gBjg62OttV1pRhdd++2dcuZo9gUckpNu4tqmlDWWVNWNsPb3yC4TNXh0dHeDl6SFSNjQbSKVSbFmdjLVLFuutyyupxEefn8LQ8OxtdEjjJ5FIEB7kK4gVV9SKlA3R1HHQGQ9tbO0cY0vTpvu4rCzMIZdJcSO/GMePH8fx48dHLWycHxGIy3v+F3/4+TdZ2EhERERENEuxuJGIaBYoLKsWLC9LSYBUOv63eN0upZEzqLjR3MwMKQmLBLGCksoxtp5epeWVggIDqVSKiLAQETOi2cLc3ByP79yKRfNj9NZdyriGQ1+ehEqlEiEzmmnCQwMFy63tHejo7DL4uD29fejt7TP4OETG4OPmKOhOrlarUd/QJGJGU0Oj0aC2rgEA0NfXh+LiYrz5zvvY8OAOfPbZZ2hra9Pbx8vdFQf+/t849Nb/wM+bNxMREREREREREdHspFSq8I+Pj0CtVuutMzMzw65tm/DPr3wTSYsXwMzMbNRj1NU3YHBwUBCLjAgbdw62NtZYv2oZfvmT72N5SoLe+hGFAh/sPYRT5y6N+5h3Cw8Ngoe7qyBWVCb+mKhGo0FBkbC4MSYqYkbMKEmmTSKRYHnifGzfsFzv3oaquia8s+8ouns4VkVARLCfYLmmoRmDQ8MGHVOlUqG2oQXtnd0GHYfIWPzchbOR1jS2jrGlaav9eobG/v5+lJSU4NLFVAQlPoC3330flZX633ssLczx//34WWQe+D8kLYiY7nSJiIiIiGgaycVOgGYKQy88cwapOYOzhc04XT196O4Vdq2Ki40e9/4ajQZFJaWC2P26lE63uPnROH3hsna5qbUDw0o1LMzN9TeWTF/dfn6hcCAvKMAf1tZidQgz4LWpNnT2Kw5eTgWpVIqtm9bC3s4W5y9nCNblFBShr38Ajz68BZaWFiJlON34+TMaHy8P2FhboX/gzg0ZJWUVSE6Im/Cx1Go1ikrKkXH9Jqpr6wEA4SFBWLU0Ed5eEy2aMuDnZcgNEYb+mhj0dibi92lDnjP9e4EmRjr6jULjYja+z0wLM8DDxQFNbV3aWE1dA4IC/MbeycR0dHTiyNETOHb8BOrr69HV1XXP7SUSCV5+ejt+/c8vw8He9uvoOH6HpAZcApAaMCOkxMDZJDWG/KKK+D1FzBu8DPm7zeC8DTi3Ie/jYl7WEPM543dxIiIiIqJpZsh3cBH/VpuT43tzdfzbgMc9Q4vVFAol/vHpEZRW1emtCwn0x9O7t8PdzeW+xykqFo6H2traTGr2QUtLCzy+cyvi5kfjo32H9RoOHvrqJAaHhrB109oJFQBKJBLERkWgueVOk7Oa+qZ7/Fym5+fV0tKG9g7hLEvRkeGTP6BB17MNu6CtURkwE6Ah10cNur5phP0NMQ1jNguiQ2FrY4W9X57DiOLOuHVLexfe3ncUjz6wGj6erhMsqDXgngGJiM+3xkTnKDDo8+P+z3ewnyfkcimUylvNf9VqDUorazA/KgTAxF6b/QODuJ5bgqs5RejtHwAAhAb4YFXyQvh6uU84+8kT62dt4O+3IeM9ht7LozHg92wCzeGNel4AEtn4mlb7ezoBOXca29c06jc9NWWdXT24kH4dv39nH3ILitHZef+ZKdctWYC//OKbCPH3BKABVIbe23QfhoyjAgb9jms0k29uLpng+6BRzcw/H4iIiIjIRLG4kYjIxNU2tAiWLS0s4ObqPO79m5tb0NPTK4hNpEvpdPDx9oRUKtV2Y9VoNKhraEZIoHhFDSMjIygtF3YNM2ggj2gUEokEq5Ylw97OFl+ePCfoSFxRXYP/eePvcHV2gqe7Gzw93ODh5gJPdzfY2tqImDVNJ6lUivCQIGTnFmhjxWWVEypuHBgYRFZOPq5m56Jb5/OgpLwSJeWViAgNxqqlifDynM5BPaI7/L1cBMWN1bUN4iVjBP39/bh4+QrOnL+IsxcuIvtmLjTjvMkwOMAXH/zuF1gSHzvFWRIREREREREREYlLpVLjnX1foaCkShCXy2R4+IH1WLUsWW/Gt7EUl5YJliPDwwyafTAyLAQ//9F38dmXJ3DpyjXBuuNnUmFuboZNa1dO6Jj+vt6C5dq6Rmg0GlFnSdSdtdHBwR7eXp4iZUOzVUiAD77xyGZ8/Pkp9N3V0LOnbwBv7f0K5mZyeLg6wdPNWfvP3cUJZma87W0uMDOTI8TfG8UVtdpYcWXd18WN49PU2o6M7ELkFldAqRIWEZVV16Osuh5hgb5YlbwQPp5uRsudaLz8PBwFy+1dvegfHIKNlaU4CRloYGAQl6/exJlLGTh7KRNZuUWjzsA9GhtrK/z1376JJx5YzpmiiYiIxksimbFNq6YVnwMik8arPEREJq5Gp7jRz8dr3IN4AFBUIhzIs7Kygp+vj1FyMxZzMzN4erihobFZG6upbxK1uLGsogqKuzpHSqVSREXMrBkvafZYtGAe7Gxtse/zo1Ao7/zeaTQatLZ3oLW9A7l3zSRqa2MDT3dXQdGji7PThN4byHREhAqLG6vr6tHe0QknR4d7/sybW9qQmXUTOfnFUCiV9zxHcVkFissqEBkWjFVLk+DpwUE9ml7+Xq7IzC3XLtfUm1Zx48jICDKuXsfZC5dw5nwqrmReF3yPGA8bGxvExs7Dkb//Cq5O9lOUKRERERERERER0cygVmvw0aETyCksF8QtzM3xreceR2T4+ItaAKCoRDhzY2S44c1eLS0t8MSubXB3dcFnX54QrDty7AysLK2wcmniuI+nW9zY09eP7p5eODqIcz1Qo9Egv1BY3BgdGc5CA5oSXu4uePGxB/DRoVNo6+gWrBtRKFHb2IraxlZtTCKRwMXJHp6uzoKiR1sbq+lOnaZBRLCvoLixrKoeTa0dcLC3h6WF+ajvS2q1GkXlNci4UYjq+qb7nqO0qg6lVXUID/LDquSF8PZwNepjILoXbxd7yGVSKFV3CgBrGtsQFewrYlbjp1AocPVGPs5cysTZS5lIv56DkZGJjYVaWVkhJCQEv/z2o9i5KmaKMiUiIiIiopmKxY1ERCZOt7hRd9DrfnQH8sJDg2dkAZS/j7decaOYCopKBMuB/n6wtrYWKRuaC8JCAvHc4zvwycEv0D8wcM9t+/r7UVbZj7LKam3MTG4GdzfnWwWP7m7wcHeFh5sLLCwspjp1mmLBgX6Qy2TaLqNqtQZ/fPNDyGRS2NvZwsHODg72t/45OthDLpchO6cAlTV1Ez5XUWkFikorEBUeglVLk+DhzkE9mh5+ni6C5camFrS2dUxoturppFarceNmrraY8WLaFfT33/u9W5eNjTX8/Pzg6uoGX19fODo6YlFUIAsbiYiIiIiIiIhoTrhwJRuZNwoFMUtLC3zvxacRHOg/4eMVFQsbvkZMsDjyXtatWgorK0t8vP+wIL7v86/g5+M57nxdXZxgaWmBoaFhbay6rlG04sbW1ja0tXcIYjFREaLkQnODo70dXtj9AD49fFrvPghdGo0GbR3daOvoRl5JpTZuY215q9DxrqJHFyf7GXkPBI1fWKCwwGt4RIG/ffwFIJHA3MwMjva2cLCz+fqfLdRqNbLyS9Hd2zfhc5VU1qKkshYRwf5YlbwQXu4u99+JyEAymRQ+bg6oburUxm4UVc7Y4ka1Wo3cwlJtMWPqlSz0TXAs1MLcHIsWxEBqbg1fX184OTnBTCbFhkTDG1AQEREREZHpYXEjEZGJq23UKW70m1hxY7HOzI2RETPzIlGAnzeuXMvWLtfUN4qWy8jICEpKKwSx6MhwkbKhucTH2xMvPPUIjp46j/KqGmg0mnHvq1AqUN/YjPq7ioQBwNnREZ4et2Z5jAoPgZsrB2dMjbm5OYID/VBSXiWIq1RqdHb1oLOrZ0LH8/X2RNKi+ejp7cPljCwMDA7qbVNYUo7CknLERIZh68Y1sLRkkSxNrQBvV1hZmmNwaATArZsWLl65ih0PbhQ5s1s0Gg1KSstx9sJFnDmfinOpl9HR0Xn/He8il8vh5eUFHx8f+Pj44MVnH8eRY2cE26xYxO8bREREREREREQ0+7W0deLwqUuCmJlcju88/+SkChsBoLhUZ0zUCDM33m1p0mKMKBTY//lRbUyj0eDDvZ/jp//0bZibmd33GFKpFH4+Xii963p/bX0jFsSIU1CYky8sLrW3t4OPt5coudDcYWVpgae3b8D5Kzdwo7AM/QNDE9q/f2AI5dUNKK9u0MbkchncXRzh6eaM8CA/RAT7cQZSE2Nnaw0fT1fUN7XprRtRKNDS3omW9vGNy8ikMsyLCEJ8bARqG1tw+Voe+kcZDy2uqEFxRQ3mhQdh27qlMDe///s4kSGiA90FxY2ZOaV4eG0SrCzMRczqFo1Gg/KqWpy5lIlzl67i7OVMtHV0TegYcrkcHh4e8PX1hY+PD9avSER9cwfau3q128SFecDOWvzHS0RERERE04/FjUREJkyj0aC7t18Q8/Jwn9AxdGdujAgLNTivqaD7uLp7Jt5hz1BDQ0PIupGLK1ezMKIY0cYlEgmiZmhRKM0+zk6OeOrRhzEyMoLm1jY0t7SjqaUVTS2taG5ph0KpmNDxOrq60NHVhYLiMpy7dAXxC2OxZnkKrKwsp+gR0FRYOC9Kr7hxImQyKWIiwpC4aD58fbyArwtnE+JikZmVg8uZWRgc1B88zi8qhUqlwmM7Hpz0uYnGw0wuR8qCMJzNyNfG0jOz8ODGNeO6KWgq1NU34Oz5W8WMZ85fRH3DxBovyOVyJCUswpqVyzE4okJP3wBkMhkAwN3NBdW1DYLtPV0dEObvYbT8iYiIiIiIiIiIZiK1WoOPDp2EQqEUxF98ZjdCgwMndcze3j6963dTMSa6elky+vr6cez0BW2subUNXx4/ix1bx9eozdPdTVDc2Ns3sVmQDKVWq1FcUoa0jGuorasXrIuODGdBGE0LMzM51i+Px7pli9HXP4im1g40t3WiqbUDTW0daO/smVATWKVShYbmdjQ0tyMrrxQBPh54YHUy3F2dpvBRkLEtnhc2anHjeNlaWyFhfiQWx4bD1sYaAODn7Y742AhczSnC5Wt5GBjSHw/NK6mErY0VNq1MmvS5icZj6fxAnMgogfrr97cRhRKZOaVYmRAjSj7Nre04fTEDZy5m4uylDNTUN01of5lMhoSF0VizLAnhIcG4nFsJufzO7cqB3u7IKa4W7LM81s8ouRMRERERkelhcSMRkQlTKlV6MQvziXWwKi4tFywbu0upsZjrPC6FUjnGlsbX3d2DK1ezkHUjF8Mjw3rrA/39YPP1xW+i6WJubg4/H2/4+dyZrVWtVqOjswvNrW1oam7TFj329vXf40h3aDQaXM3OQUFxGdavWooF86I4SG0ioiJCsX7VUmTnFKCruwdKlf7nw2hsrK0QvzAW8Qvnwc7OVm+9ubk5liXHIyFuPjKzbiLtarZekWN1XYPefkRTYdmiSEFxY//AILJu5iM5fuG05lFTW4dvv/oTHD1xesL7LoiNwdpVK7B21XIsX5oCOztbdPf04mevva4tbASA6PBQXEjLFOy7Ii6M78lERERERERERDTrXbiSjfJqYVHdyiWJiI2e/OyFJWXC8VCJRIKw0OBJH+9etqxfhYLiMlTX3nkMZ1LTsDA2alyzTuo2c5uuMVGFQoEbOXlIz7iOjs7RZz+LiRJnBkmauyQSCexsrWFna42wIF9tfGREgZb2rq+LHju0xY8jivG9Xqrrm/G3T44gaWEUViUthMUMmBWN7i8uJgwKpQp5xZXo7u1Hb/8gxlPi6uPhhuS4aESHBQjGYm4zNzfD0vhYJMyPRObNQqRdz9crcjSkqJJovJzsrBAb4ombZXcaMqReK8CK+OhpHSMcHBzCz/7nT/jTO3uhnOD3kNioMKxZloi1yxKxInkR7L++B+HN/acEhY0eLo7IyBE24/dxtUWQp4PhD4CIiGgukkhv/Zvr+BwQmTQWNxIRmbDRBrPMzMb/1j44OIiq6hpBLCIsxOC8poLu41IolNBoNFN6Aa+xqRlpGdeQX1gMtVo95nbJCYumLAeiiZBKpXB1cYarizNiIsO18f7+gVszO7a2o6n5VsFjW0fnmL/X/QMD+PzoKWTl5OOB9avh4e46XQ+BJkkikWBp0mIsTVoMjUaD/v4BdPf0orunF11f//f2v77+ATg7OmDRghjERIYJBhHGYmFhjuUpCUhctADnLl3BlWs3tOvu9f5IZEweLg6IDPJGUeWdgtrUyxlIWrxgWgb0NBoN3v3gE/zg//0cvb3jm0E6NCRIW8y4esUyuLq66G1z+co1wevIzEyO3IJiQddpC3M5EudNzc1WREREREREREREM0VLeycOn7okiLk4O+GhB9YbdNyiYuHN84EB/rC0tDTomGORyWR4Zvd2/Pfv/qptRKjRaPDh3s/x03/6tl7xoi653pioYkryvK2/vx+Z17JxNesGBgYGx9wuMMAPvnc13CQSk7m5GXy93ODr5aaNaTQadHT1CGd5bO1Azxizn6rVGqRnFSCvpAoblycgJjyQDQZnOIlEgqSFUUhaGAUAUKlU6OkfRndvH7p7+tHd24+unj509/ZjcGgY7q6OiI+NgK+X+7iOb25uhmUJ85EwPxLHLmTgRkHZVD4colGtWBgkKG5sautEaXUjwgOn5zM4IysXz736SxTfNYv0vQQH+GLNskSsWZqANcsS4e7qrLdNZ08fcoqFx3NysEFRhbCZxYpYv1vvwxOYmZeIiIiIiGYPFjcSEZkwQ6/nlJVXCm6cB4DwGVrcONowgkYDGHt8QaPRoKy8EmkZ11CpU/ipy9/XByuXpSAkONC4SRAZmY2NNUKCAhASFKCNKZVKtLS2o7m1DfWNzbiZVwSFUjhAXlPXgL+//ykSF83H6mXJsLCwmO7UaRIkEglsbW1ga2sDH29Pox7bwsIcMZFhguJGoum0Ij5KUNxYVVuP6zfyEB8XO6XnbWhswkvf/eF9Z2v08vTQFjOuWbUc/n6+99y+o6sbp1PTBDFXZyc0NrcKYluWzoeVxb1veiIiIiIiIiIiIjJ1X5y6DIXOzGtPP/owLA0cnyguFRaoRIaHGnS8+/HydMcDG1fj8NE71xObW9tw6co1rFmecu+ddcZuJaOOkhqura0d6ZnXcTM3/56zMtnb2yE5YTES4+NY+EUzmkQigYuTA1ycHBATHqSNDwwOaQsdr+YUobNb2Lywt28AB45dQFZ+CbasSoars910p06TJJPJ4ORgBycH4/7MLCzM4eflzuJGEkW4vxvcnWzR0nnnvergyXT88wvbIZNN3UxEw8Mj+PfX/4bf/OX9ezY39nBz0RYyrl2ehEC/+xddHjyZDvVd32/M5DK9wkZfNzskR7GJAhERERHNXn19fejq6gIAODo6wtbWVtyEZiAWNxIRmbDRZmnUHfC7l6ISYZdSXx/vGfthOaLzuMzkckilxhtAUyqVyM0vQvrV62hpbRtzO4lEgqiIMCxJimd3UjJpcrkc3l4e8PbyQNz8GCxLjsfxM6koKi0XbKdWq3Hl2g3kFZZi45rlmBcVzsFrIhLNvDA/ONnboLOnXxvbe+grhIcGwd7O+N9hNBoNPtl7EN//8U/R2dmlt97BwR5rVi6/Vcy4cjkiI8LG/R6p0Wjwyf7DGBoaFsR1CxuDfVyxJiFi0o+BiIiIiIiIiIjIFHT19OkVkqxckojw0KAx9hg/3THRyPAwg495P+tWLsWN3EJU1965eT/1ciZWLU2CVDp2cYJCp9BwtPHgydJoNKiprUNaxnW9gk9dnh7uWJKcgJioCMhkMqPlQDTdrK0sEezvjWB/byTMj8Sla7m4dC0XKpWweKeiphF//fgwUuKisCJxPszN2XCQiKafVCLBioVBOHAuVxura27HicvZ2LJi8ZScMyunEM/94JfIK9L/bmBhYY6NK1O0xYzR4cETul8kq6AC2YWVgphcLoNCqdIuS6USPL0uZkqLN4mIiIiIpkthYSHS0tKQlpaG2tpadHd3o6enZ9Rt7e3t4evrCz8/P2zZsgVLliyZsXUc04HFjUREJkw+ykDSiEIxypaj0+9SOvUDeZM1ohgRLMvlxvkIGxwcxLXsHGRcy0ZfX//oU0QCMDMzw6IFsUhOWAQnJ0ejnJtoJnF0sMdjOx5ESVkljp25gM6ubsH6vv5+HPziOLJu5mPL+pVwc3URKVMimstkUil2rE/E2wfPaWP9AwP49OAX+Oazjxm1+LqlpRUvv/oTHDry1ajrv/H04/jd/7wGBwf7SR0//Wo2CoqF38UsLMwxPHznO4+ZXIanHki5581OREREREREREREs0HatVzBTEFmZmbYummtUY5dpHMdLmKKZ24Ebs0otv3BDfj9X9/Vxlra2lFSVonI8JAx9xsZEY71yo1Q3KhWq1FYXIq0K1dR39h0z21DQ4KwJCkBQYH+bHZJs46ZmRyrU+KwICoER89noKxKOHOYSqXGpWt5yC2uxKaVCYgM4euAiKbfsvmBSMutRkPbnRugj13MwvzwQPh6Gu8+DYVCgV+/8Q7+6w9vjTqLc8LCGLz3h/9AVFjwpI7f2z+IvccuCWJyuQyDQ8L7vzYnBMHHlbPmEhEREZHpOnnyJI4ePYr09HRBIaPmrhnMR9Pd3Y3u7m4UFBTgxIkTAAA/Pz8sWbIEL774Inx9fac075mGxY1ERCZMKpXA1sYKff2D2lhzSxt8vDzGtb/+QN7YA2lia2ltFyzb2VobdLzOzi5cuZqF7Jw8vUHCu9na2CApfhHiF82HlZWVQeckMgXhoUEICvDF5YzruHTlOpQq4UXsyppa/PXdT5ASH4eVSxNhbm4uUqYkFt0/uDioS9NtUVQQsqIqkV1YpY3dzCvE9Rt5iI+LNco5Dhw6gm//4J/R1taut87Twx1v/vl3eHDzhkkfv6OrGweOHBPEZFKpoLARALauWAAP58kVTxIREREREREREZkKlUqFS1dzBLHERfNhbW342JxKpUJpeYUgFjkNxY0AEBYcCC8PNzQ2t2pjF9Iy71nc2NjUIlh2tJ/8zf4jIyPIvpmH9Mxr6OoevUM8cKsQMzYmCkuS4uHu7jbp8xGZCmdHezz50DoUldfg+IVMdPf2C9Z39/Zj75fnERrojc0rE+Hi5CBSpkQ0F5nJZXhqYxx++0kq1F+PzavVGnx45Dz++YXtRpnhMK+oDM+9+ktk5Rbqn99Mjn/70cv45+88a1Dj+b3HLqFvYEgQU941YyMA+LrZYcNiw2fpJiIiIiISw9tvgh2NXQABAABJREFUv41//OMf2oLGydxbq9FoBPvV1NSgtrYWe/fuxZIlS/CTn/wEkZGRxk18hmJxI4nOkBvi71fNTDONiD8vMX9Xprjow8/LHYVl1drlmrp6LFoQM659i0pKBcszeebGmroGwbK/jxcg1Z+58n7qGxqRlnENBUUld95DRvkRubm6YElSPGJjoiZwsU7EAh+N6v7bjLWrqO+lhpybBVVTxczMDKuWJWN+TCSOnb6A0ooqwXq1Wo3LmdeRW1iMTWtWICoidGYXuBn6Oz6TH9u9zMXvSYb+rMR8ygzK3YDEJYY+aPX9Nxn75JPec/emJSitakTf4LA2tvfQlwjw84Gbq/Okj9vR0Ynv/dO/4NP9n426/olHd+KN3/4aLi6TP4dSqcQHn36GoaFhQVylFj6XwT5uWJ0QBUjuGqCUGDhYadD+JvpeKOrfIAbsK+pnj6HPman+rhhA1O8aIn6fNuRxm+r3KyIiIiIig4j13R/ifgefi9cp5yoDfs9yiyr1iouWpyQYmhEAoKa2DkNDwhvrI8Kmp7hRIpFgxZJE7D30lTaWk1+Ezq5uODnqF0up1WrU1jcKYv6+3hO+rtfb24fMa9m4mnVD77HfzdLSEvGLFiApfhHs7GwncAaR3s80hlyPNnR/Ed9HDXkPF3PcxJBzT8Nnh0QiQVRoAEL8vZGamYP07HyoVGrc/aDLqurxl5ojWBY/D8vi58HsfjOpztVrXgY9bsOLtCbtXnlLJBC87iWSmfPzNSiPid9jIzy3AT8vQ8e4DHofnvz9NGK9/wd4OmHd4kCcvFapjdU1t+PzMxnYuSFl0sdVKpX47V8/wL+//rdRG8EviA7He3/4DyyIiZj0OQDgcnYRsgsr77mNTCrB02ujIZNKhO/7Yn3uSUR8fRjEwO9HBr0viHluERnyPUXUsTUTvUfPVMcjTTVvMl0z6fuqmPgc0DR5/fXX8dZbbwEQ3oM/mfuoR9vn9jHT0tKwfft2xMTE4LXXXpv1RY4sbiQiMnH+3rrFjQ332PoOjUaD4lLhzI0zubixurZesOzv6zXufTUaDUpKy5GWcQ3VtXX33DYowB9LkuIRGhI0s4u1iKaBs5Mjnti1DcWlFTh25gK6e3oF63t6+7Dv8FGEBPpjy/pVcHF2EilTIppr7GyssHtjAt7+/JI21j8wiDf+8T5+9N0X4Ogw8dkOvzx2Ei9994doam7RW+fq6oK//eE32PnwVoPyVqvVePeTgygpv/dgnpO9NZ5/eDmkUhEH9YmIiIiIiIiIiKbJxcybguWgAN9bRX1GUFwiHA91cLCHh4e7UY49HomLF+Dzo6cwPDwC4Na45eWM63hw4xq9bZtb2jCiEBYaTOR5aGlpRXrmdeTkFUClGruQw9HRASmJ8YhbMA/m5ubjPj7RbGRuboZ1yxZjYXQovjqXjspa4f0WKrUKFzJvIqeoAptXJSI8yFekTIlortmcGIzcylY0tvdpY2czcmFnY4UNSxdO+HjFZVV47ge/REZWrt46mUyGn37/efz8By/B3NzMkLSRXViBT7+6eM9tJACeWhsDH9fJz1BNRERERDTdCgsL8eqrr6K2tlZbgHiv4sSJun2s2/+9fZy8vDxs374dP/nJT/D8889P6timgMWNREQmzt9bOPhWU9cIjUZz38K8hsYm9PUJO6BGhE9Pl9KJUiiVaGgSFhqMp7hRoVAgJ68Q6ZnX0NbeMeZ2UqkUMVERSElcDG8vT4PzJZpNJBIJIsNDEBzoh4vp15B2NUtvQLy8qgZ/eedjLE1cjOUp8TAzM+xiNxHReCyKDEBWZA2yi2q0sfaOTrzx9/fx6svPwcF+fINh3d09+ME//wzvfbRn1PU7HnoAf/39b+Du7mZQviqVCh/vP4zsnPx7bmdnbYnvP7YeTvY2Bp2PiIiIiIiIiIjIFCiVKpRVCZuTrliSaLTjF5WUCpYjwkKntcGplaUlEhctwMX0q3dyKi0ftbixpl5YVOXoYA8723tfJ9RoNKiqrkFaxjWUllXcc1tvL08sTU5AVGQ4G6sR6XB1dsAzOzYiv7gcJy5eQ2//gGB9Z08vPjlyBhFBfti0MgFODizImUvYF5vEYCaT4ul1Mfjt/kyo1Xdujj58NhNymRRrkueP6zhqtRpvvP0p/vW//4ihoWG99dHhwXjvD/+B+AUxBuecU1yFdz87e9+buR9dGYmECN6fRURERESm4/ZsjaMVNd79/dfe3h6+vr7w8/ODn58fHB0dYW9vDwcHB8Hxuru70dPTg66uLtTW1qK2thYFBQWCbXTP8Zvf/AZHjx7Fe++9B1tb26l4mKJicSMRkYnz0yluHBgcRHtHF1xd7j2Dmm6XUhsba/h4j382xOnU2NSiV0zl5z32Ra6BgQFczbqJzGvZ6B8YGHM7czNzLI6bj6T4ODg6Ooy5HREB5ubmWLtyCRbMi8TRUxdQUV0jWK9SqZCanomcgiJsXrsSEWHBImVKRHPJk5uS0NrRi7qWTm2sqaUVr//5Lbzyzefu+33o5OlzeOE7P0Bdvf7M105OjvjT6/8fHn90h8E3OymUSrz78QHcyC2453a21hb43mNr4eEy8ZkniYiIiIiIiIiITFFDSxuUOuOA86MjjXb84tJywXKkCM1e58dECoob6+qboFar9QoMa+qE1ynv1exVpVKhoLAEaRlX0djUfM/zR4SFYklyPPz9fKe1sJPI1EgkEsyLCEJYoA/OZ9xExs0iqNVqwTbFlbUor2nAisT5WLIoBnK5TKRsaSpNdpYNImPzd7fHE2ui8dFpYfPUg6euYHB4BFtWLL7nZ3tFdR2e/+G/IfVKlt46iUSCH3/7Wfzqxy/D0tLC4Fwzc0rx4ZHzUN/n9bN9aRiWx3IWXCIiIiIyHS+88ALS0tIEk0/d/rvR3t4emzZtwtKlSxEdHQ0/Pz+DzpWeno7Lly8jLS1NW+wokUggkUig0WiQl5eHtWvX4rPPPoOPj49hD2yGYXEjEZGJc3awg421JfoHhrSxnIIirFmecs/9dLuUhoeGzNgOnTn5RYJld1dnWFlZ6m3X29uH1MtXcCM3HwqFYszj2dnaIjlhERbHzYelpf5xiGhsri7OeHr3wygoLsXxM6no1ZkBtqu7B59+9gXCQ4Kwed1KOLFwmIimkJWlOb63ew3+7+OTaOno1cbb2jvx2z+9iScfeQix0RF6+/X19eEnP/sV/vbWe6Med8vGdXjzz78zyozOLW3t+HDPIZRX1dxzOyd7a3z/sXXwcOH7JhERERERERERzR219cLCPLcxxgEnS2/mRhGKG/19vQXLIwoFmlra4O15p4mtWq3GzTzhmKi/j35xo0ajwfXsm7iYloHu7p4xzymXy7EgNgYpiYvh6upi4CMgmlssLMyxcUUCFkaH4uj5DFTrvE8pVSqcTc/GjYJybFmViNDA2XUzIRHNLMlR3hgcVuLgxWJB/GhqFprbuvDo5mWwtRZ+d9JoNPjbB/vxz//5e/QPDOodMzTID+/9/j+wJGGhwfmNKJT48sI1nEnPued2EgCPr4nCkmi+ZxIRERGR6XjhhRdw+fJlANAWGALAxo0b8a1vfQvR0dFGPV9KSgpSUm7VgPT29mLPnj1488030dPToy2s7O7uxo4dO2ZdgSOLG4mITJxEIkFksD+u55VoYxfTrmL1suR7dufSnbkxMjxsynI0hEqlwuWM64JYRFiQ3nZDQ0N46/1P0N0z9iCeh7sbliTFY150JGQydlAkmiyJRIKYyHCEBgXgQlomrly7ode1tKS8EhVVtVieEo+lSYshl/NrJxFNDTsbS7zy2Fq88ekZtHTeKXDs6e3DX9/5GMnxC7Fr22ZYW1sBAC5cvIxvvPwqKquq9Y9lZ4vf/89r+MYzTxjcwVytVuPC5Ux8fvTUPZsuAIC/pzNe2rEKzg42Bp2TiIiIiIiIiIjI1NQ0tAiWdQsBDTUTxkTtbG3g5OiAzq5ubaymrkFQ3JhXWIKOzi7BftGR+oWYVzKv48Tpc2Oey8rKEomL45CwOA62trzeSGQID1cnPLdzI3KKKnDy4jX0Dw4J1nd09+Cjw6cRHRqAjSvi4WBvJ1KmRDTbrV7oD6VKjcNpwqYN1wsqUFLdiMc2L8PCqFv3UtXUNeLFH/0Kpy9mjHqs77/wOP77p9/Xjp0aoqKuGR8dOY/m9u57bmcul+LZDfOwINj9ntsREREREc0ktwsb756tcePGjXjttddgZzf11wDs7Ozw0ksv4aWXXsLevXvx+uuva4scbxc4njlzBra2tlOey3TgXeZERLPAsoRYQXFjc2sbSsoqEREWPOY+M6FL6XjczCtCd0+vILY8aZHedlXVtWMWNoYEBWJJUjyCgwIMLlQgojssLCywYfVyLJwXjaOnzqOqtk6wXqlS4tylK7iZV4TN61YiLCRQnESJaNZzsrfBD59ajz/tPYf6lk7BuivXbqCwpBzbH9yAT/fsxx/+8g9tB6W7rVu9Em//9ffw9/M1OJ+WtnZ8tPdzlFXqF1DeTSaVYsuy+VifHAOZbGbOoE1ERERERERERDSVahqEM6L5+xivuLG7uweNTcLji9Xw1d/XW6+4MTl+oXY5NS1TZ3svBIxS6FlcWqYXAwBnJyekJC3GwvnzYGZmZpykiQgSiQQLokIQEeyHs2nZuJpbrDfGUFBWjbLqBqxMWoDkuCg2WiaiKbF+cSCsLOTYc65QEO/tH8SbB05hUXQwFP1d+Jf/+gN6evv09g/088Y7v/sVVi2JNziXEYUSX56/hrNXcqA/6irk62qHb29dCAcbC4PPS0RERGORALw3GrfmiiYyjh/84AfawkaNRgN7e3u89957Rp+pcbx2796NLVu24Gc/+xlOnjypLXDcuXMnTpw4IUpOxsbiRiKiWSAs0Aeebs5oau3Qxi6kZd6zuLG4tFywHDlDixsvpAk7iYUE+sHX21NvO4VSKViWSqWIjY5CStJieHqw8xfRVHJ3c8Gzj+9AbkExTp67hL7+fsH6jq4ufHzgMKLCQ7Fp7Qp2LTVxurN0Es0U9jZW+MET6/DukcsoqGgQrCsuKcWGB/+O7m79rqHW1tb47a//HS+/+JzBTRCaWlqRmnYVaRnXMTKO2RqffnAJvN2cDDonERERERERERGRqVKp1GhoahPEjDlzo24hoFQqRUhwoNGOPxH+vt64mXenGKG27s41zNa2DhQUC3NdkZIw6vVKlUp4jd7Twx0rl6UgIjwUUikbqBFNFUsLc2xZnYS4mFB8dS4DdU2tgvUjCgVOXbqOGwVl2LI6CUF+XiJlSsag+15LNFMsm+cLW0szfHSmAEMjd+6T6u/vx2u//ROqq0dvvPrSUzvx21/+EHYGzurcNzCE9BtFSL1WgI5u/QJKXasX+GHHsnA2oiciIiIik3LixAkcP35cW9gYHR2N999/f1pma7wXOzs7vPHGG/jNb36Dt99+GxKJBDU1Nfi///s//NM//ZOouRkDixuJiGYBiUSC5Ynzsf+r89pYTn4ROru64eTooLf9wMAAqmtqBbGIsJlX3NjY1ILS8ipBbEXK4lG31R2sc3ZyxPZtm6cqNSLSIZFIMD8mEuEhQTh36Qoys27qdS0tLClDWUU1Vi5NREpCHLuWmqjm1nbBsq2NtUiZEOmztjTHdx5ZhfScchw8m4X+gSFcvXoVN27cGHW2xiXJifjgzT8hJDho0udUqVTIyS9Calomissq77u9XCbFpqWx2JA8j7M1EhERERERERHRnDY4NAylSiWIebi7Gu34xSXCgsHgoABYWIgza5CnzuPq7bvTKDI1XThro7WVJeIXzhv1OFKpsDhg0cJYREWGGylLIrofL3cXvPDoZmTnl+H05SwMDA0J1rd2dOP9gycRGxGEDcvjYWfLcTRT1NAsLLx3sLMVKRMifQtDPRDk5YiPTuejoLoNpaWluHTpEoaHh/W2dXdzwbu//xU2r1466fNpNBpU1bcg9VoBsgoq9L67jcbCTIbnN8YiJtB43+uIiIiIiKZDb28vfvGLX2gbdGzatAm///3vxU1Kx09+8hM4Ojri9ddfBwC8+eab2L17N3x8fETOzDAsbiQimiWSFkTi8KnLGBm5NUuQWq3G50dP4RtP7NLbtkRn1kYACA8LmfIcJ0Kj0eCzL4XTJNvaWGNhbNSo28t0ihs5sxiROCwtLbB53UrExUbjq1PnUFvfKFivUCpw+sJl3MgtwJb1qxAc6C9SpjRZNXXCGfH8vNl5lmYWiUSCJQtCMTzQh6de/Q80NrfobSOTyZCUlIRFcXE4eT4NAWVV8PP1hr+v931nlx0aHkZdQxNq6hpQU9eA4tIKdPf03jcvJztrLIsLwZKFEbC3sZr04yMiIiIiIiIiIpotFAqlXszC3Nxoxy8qKRUsi9ns1dzMTLA8org1ptvU0ooLl4XFjSkJC2FuLtz+Nt3GkWq1flM3IppaEokEi+aFITLED2fTb+B6Xoleg8Xc4kqUVNZhVfICJC2M4syqJqa2UTi25OflLlImRKNzsLHArqXBePLIlziXfmPUbSIiIrB06VJczq1GU+cQ/Lzc4O/lCj8vV1hZjP19a0ShRH1LB2oaWlHT2Iqq+hY0tXWNO7eUaG88ujISZmzySkREREQm6Oc//zl6enogkUgQHR094wobb3vppZeQm5uLkydPQqPR4NVXX8WBAwfETssgLG4kIpolrCwtkLggEpeu5mpjV7NysGh+DBbMExYEFpcKu5QG+PvB2npmdQy8cjUb+UXCAceliXEwk4/+0aU7GMCBPCJxeXq44fknH8GN3AKcOn8ZA4ODgvVtHZ34YO8hzIsKx4ZVy2B/n2Iimjl0C1b9fFjcSDOLQqHEf/3pffzXHz+AapTOoe7u7lizZg2cnJygUquRW1CM3IJi7XpHB3t4e7rD0sICZmZmkEgkUCqVGFEo0NrWjqaWtlFngRxLRIA7Vi2OQEyI161mDNLRb0oiIiIiIiIiIiKaa0aU+sWNcjPj3caiOyYaGR5mtGNPlJlOcaNCoYRarcaHew9BedfzIJEAy5PjxzyO7pjoaNdAiWh6WFtZ4sE1yYiLDsVX566goaVDsH54RIETqddwo6Acm1clIsDHQzvzA81cff0D6OgSNrX082ZxI80sn526gu/86m9o7ejRW2dlZYWVK1ciKCgIANDZ3Ydr3X24ln+nEb67swMc7KxhJpdDJpNCpVZDqVShb2AITa2dUE9gLFR7TEcrPLs2EgFeLpN/YEREREREIqqtrcWJEycgkUhgb2+P999/X+yU7umNN97AunXrUFdXh/z8fBQWFiIqavRJpEwBixuJiGaRzSsTcT23BINDw9rYpwe/QGhwAGzuKl4sKhEO5EXMsFkbO7u6ceDIcUHM3s4W61amjLmPbpdSDuQRiU8ikSBufgwiw0JwJjUN12/m6RUE5RWWoLCkHBGhQVg4LxqhwQHsXDqDdff0oqe3TxBjcSPNJLlF5Xjun/4L2fkleuvkchlWrViGkLDIe77PdHX3oKtbfyBwvKQSCTxd7REX4YeE6AC4ObF4m4iIiIiIiIiIaDRTXeJTVKwzJhou3syNGgjHRyQS4MyFNFRW1wniq5clwd1t7KIA/YavauMlSUST4uPpihd3b8H1vFKcScvG0PCIYH1zWyfeO3ACbs4OiIsJw/zIYNjaWImULd2P7qyN5uZyeLg6iZQNkVBHVy9e+a+38OlXF0ddv2h+NBbGJ8HCwvKex2np6EZLR7dRcrK1lGPVfB+sjfPjbI1ERERikUgACT+HwWYyZKC9e/dq///3v/89bG1tRcxmfP7whz9g586dAG7l/+///u/iJmSAmVHcKJEY981kEp1jyFCG/Pzm6s9LpMfN18es5mhvi12bV+DDQ6e0sZ7ePuz7/Ci+8cQubay4ZOZ0KdWl0WjwyYEjGBwaEsSf2PmAoEBTl1Qqw93vRbdmbjSlL6oGvjY1HLikmcvKyhIPblyNRfOj8dWp86hvbBasV6lUKCguQ0FxGexsbbAgJhILY6Pg6uJ8ZyMxP78MOfcs+4O5pq5BsGxlZQlXl9EG88T6bmjg823Iz8vg92EDHrch5xbz88PQi2rSO3/OKpVK/OZvH+Pf/+9NKBT63d4XxoTjvd/9ArGRoSiva8HF60XILqqByog3/ni7OWJNYjTio4NgJpeNvaEhv2eGPmem+p4k5meAQc+ZiO9nYjLoc9OQ8xqwr6HnNtmfl4hP2py9NjHa456rzwURERERzRkGXXMT8/uyqV4TMK3nzEyuf8uKQqEYNT5RKpUKpeUVglikiMWNSp3rllKpFF+cOCuIubs6Y9umtfc8jmxGzNxoyPVsA/ZVi9jc1qBr6aZ67QiGXZPWGPDzEvVa9j2u79+DVCZDwoJoRIcF4fTl68jOL9PbprWjBycvXsfpy1kIC/TFwuhQhAf53tXIWcTfcYPGLib3nM1UtQ2tgmVfTzf9ppliPmZDXpeGjjOZ6pikWn/s0CTovC6/On8N3/rlX9DY2qm3qYujHf78b9/CI5uWYmhYgRNXK3E5vw4Dw1P32EO9HbE81g8LQtwhv7uo0eD3cAN+T6WGvDYNvddg8nkbNqsvC1loGhj6t/mcHIc1gKjPNxHR3LZv3z5IJBJER0cjJWXsCZlmkpiYGGzYsAEnT57EsWPHWNxIREQzR9LCKGTnlyKvpEobu5qVg3mR4UhYNB8AUFRSKthHzC6lulLTMpFfJMwvcVEs5kdH3HM/qVT4VxlnbiSaeby9PPDCU48g62Y+zqSmCWaZva23rx+XMq7jUsZ1+Pl4YeG8KMyLCoOFubkIGZOu2vpGwbKft5eBF9qJDFdcXo3nfvgfyMjO11snk8nwr99/Dj/7/nMwNzcDAIT6eSDU1w271g2ioKIRtU3tqGnqQG1zB0ZGKYwcza3ZGR3g7+kCP08XhAd4wNuNXXuJiIiIiIiIiIgmwsxM/5aVkREFrK0Mn9GsqroGIyPC2dMiwsQbEx0eUQiWBwYGobxrPFMiAZ569CHtdcyx6M/cyKY5RDOJjbUVHlq/DHHRYfjq3BU0t+kXIanVGhRX1KK4ohbWVhaYHxmCuOhQeLg6iJAx6dKdudHPy12kTIhu6esfxA9+/Tbe/ezMqOsfWpuEv/77y/BwdQQAWFqY4aElodiWEoLS+k5cyKlFSV0HBkcMv4/KxtIMcaEeWB7rCx9XO4OPR0REREQ0U6Snp6OnpwcSiQQ//vGPxU5nQl5++WWcPHkSPT09SE9PN5nCTF0sbiQimmUkEgke37YWr/3pQwwO3Rmw+2DvIVhbWyEqPATFpeWCfWbKzI3XbuRi3+dHBTF7O1s8sm3Tffe9083wFrURZ2IiIuORSqWIj4tFdEQozl5Mx43cQsHg/d1q6xtRW9+I42dSER0RgoXzohDo78tiOhHV1jcJlv18PEXKhOiWrNwirH70O+jtG9BbFxMejPd+90ssnh856r52NlZIig1GUmwwgFvfHVo6elHd2I7O3n4olSoolCqoNRqYy2WQy2WwsbSAr4cLfD2cYD7KjVdEREREREREREQ0flaWljCTy6FQ3mk61tjcAkcHe4OPrdvs1cnJEW5urgYfd7KamoXFMrpjI6uXJSE0yP++x9EbExVzJiwiGpO/jwe+9cRWXMstRnpWPjq7+0bdbmBwGFeyC3AluwDe7k5YGB2K2IggWFlaTHPGBABKpRINzW2CmL+Ph0jZEN1qjvDgt17DxesFeusc7W3wh5+9iCe3rhz1HgqJRIJwX2eE+zoDAPoGR1DV1I2CmnZUNXWjrXcIA0MKvf1us7Y0g7+bHfzc7eHvbg8/N3u42Fvyfg0iIiIimpUuX74MAPDz8zO54sDo6Gj4+fmhrq4Ox48fN7n8b+PdmEREs5CjvS12bV6JDw+d0sZUKhX+8f4ebH9gHQYGhAUAM2Hmxpt5hXjvk4PQaITdRZ/Y8QBsrO/fnVW3S6lKzZkbiWYya2srPLhxDdauWIK8whLcyCtEfWPzqNsqlErczCvCzbwiODk6YEFMJBbGRhnl5gYav+HhETS1tApi/r7eImVDBNQ1tmDbN36iV9golUrxo289gV/900uwnMDgv1QqhaerAzzv2xmZA3ZERERERERERETGIJNJ4ePphqq6Rm2spq4BUUYYuywu0W/2KubN+DX1jWOuc3d1xrZNa8d1HL0x0TEaSBKR+KRSKRIXRCFhfiSq65txo6AM+aVVUCiUo27f0NKOhpZ2nEi9hsgQP8RFhyLY30vvdU9Tp6G5HSrVnaJxiUQCHw/xCuNpbtNoNHjx538atbBx4/I4vPmf34WPh8u4j2drZY55QW6YF+SmjQ2NKDE0ooJSpYJCpYZMKoWZXAozuQw2lmYsZCQiIiKiOaOgoAASicRkCwM3btyIt956C3l5eWKnMmksbiQimqWSFkahprENF65ka2MKhQK//8vbgu1sbW3g7SXuzFsZ12/gw72f6822uHntcsyPiRjXMfRnbtSMsSURzSRWVpZIWDQfCYvmo6W1HTfyCnAzrwj9A4Ojbt/Z1Y3zlzNwIS0TQQG+WDgvClHhITAzM5vmzOee+sYmQQG6VCqFt6e7iBnRXNbXP4Bt3/gxGpqFBbdhQX5473e/RMriWJEyIyIiIiIiIiIioonw93HXKW4cuwhwInRnbowICzHKcSerurZ+1LitjTW+9dxjMDcf3ziH/pgoZ24kmukkEgkCfT0R6OuJzSsTkV9ahRsFZahpaBl1e5VahfzSKuSXVsHe1gYLIoOxMDoELk73a85IhqptFP5M3F0cJ9RIk8iYXvvLPnzyRaogZmttidf/5Xm8sGudUQoPLc3lsDQf5RZiFjUSERER0Rxzuyhw6dKlImcyOUuXLsVbb72Furo6sVOZNBY3EhHNUhKJBI88uA6DQ8PIvHGni1d7e7tgu4iwUNE6bSmUShw7dR7Hz6TqrVu5JAEPblg17mNJdR6DRqOBRqNhFzEiE+Lu5oINq5dj7YolKC2vwo28QpSUV45arKzRaFBRVYuKqlpYWJhjXmQ44uZHw8fLg6/7KVJb3yRY9vJwY1EpiUKlUuGJb/8UN/JLBPEVSXE4+uHvYG1lKVJmRERERERERERENFF+3h6C5Zq6BqMct7i0TLAcGR5mlONORndPL7p7evXilpYW+N6LT8HLw22UvUYnlQrHQDhzI5FpsbAwx6J54Vg0Lxztnd24UVCGG4Xl6O0bGHX7nr5+XLyWi4vXcuHv7Y646FBEhwbAwsJ8mjOfG2rqmwXL/j4eY2xJNLU+OXIW//6nPYKYjbUlLnz0ayyMChIpKyIiIpqxJBI2JwD4HJBBenp6IJFIEB0dLXYqkzJv3jwAtx6HqWJxIxHRLCaVSvD0zs1QKFXIzisGAHR1dQm2CfD3EyGzWwOTH+z5DA1N+t0IlyTE4ZFtmyZUoKTbpRS4NZgnl/OjjsjUyGQyRIaHIDI8BP39A8gpKEZ2Tj5aWttH3X54eATXb+bh+s08uLk6Y+G8KCyIiYStrc00Zz671dYLu2X7+3qLlAnNdT/61f/hy9MXBbGwID8cfPP/Y2EjERERERERERGRifHXKW5s7+hE/8AAbKytDTpuUbGwuDEiPNSg4xlitIJNSwtzfPf5J+Dv6zWhY0mlUsHyaA0iicg0uDg5YO3SxVidEoeKmkZk5xejqLwWKvXoRcs1DS2oaWjBsQtXER0agLiYUPh7u7Pxq5FoNBq9mRv9vNxFyobmssvX8/H8//s/QUwqleLT13/EwkYiIiIioinm6+srdgqTYmdnJ3YKBmPFBxHRLCeTyfDCY1vx8SFzpF/P1StubGhqxYmzqVi3cumoBYLGdnu2xpPnLkGtVuutX7s8GTseXD/hC/C6A3kARj0+EZkWGxtrpCTEIXnxAjQ2tSA7twC5hSUYGhoedfvWtg6cOn8ZZ1LTERoUgLj5UQgPCZqW97fZTK1Wo7ZBWNzo5zOxGy6IjOHP7+7FG299Kog5O9rjy/dfh4uTg0hZERERERERERER0WR5ubvATC6HQqnUxm7mFWFJ4qJJH7Ozswstra2CmJgzN97MKxQsS6VS/PDb34Cfj+eEj6U73sGZG4lMn1QqRWigD0IDPDEwOIS8kipk55ehcYzGryMKBW4UluFGYRmcHeyxMDoEC6KC4WBnO82Zzy4dXT0YGBSOQft5s7iRpld5dQO2v/wrjCgUgvjvfvo8HlgVL1JWREREREREU4/FjUREc4BUKsVTOzbB0d4WH3zwgWCdvYMDDh89jes38rB6eQoWL5wHczMzo+cwNDyMq1k5OHsxHc0tbaPm+NCm1Vi3ImVSnQVHK27kYB7R7CGRSODt5QFvLw9sXLMcRaUVyM4tQEVVLTQa/a7EarUaJeWVKCmvhLWVFRbMi8SKlARYcVa3SWlt68Dw8Igg5uc98ZsuiAxx7OxlvPqL3whiZmZyfPbm/yAsyF+krIiIiIiIiIiIiMgQMpkMESH+yCuu0MYuXM5ASkLcpGcjKy4Vztook8kQHBRgUJ6TNTAwiKvZuYLYyiUJkypsBEabuZHNXolmE2srSyQuiETigkg0t3UiO78UOUWVGBgaGnX7ju4enE3PxrkrNxDi741NKxLg6sxmkJNRU98sWLaztYajPQtGafp0dvfiwZd+gbbObkH8u09uwfeeekCkrIiIiIiI5g57e3uxUzCIvb09ent7xU5j0ljcSEQ0R0gkEqxOWYT+/n5B3NHREQBQ19CED/cewmdfHEdyQhyWpyTA3dXF4PM2NrUgNf0qMq7dwNDw6DOt+Xp74JlHtsHXgEKZ0WZlU6v1C56IyPTJ5XLMiwrHvKhwdPf04mZeEW7kFaKjs2vU7QcGB5F+NRv1jc34xhM7J30zxFxWWy+ctdHJ0QF27P5K0yinoAS7v/X/9G7UefN//xUrkuNEyoqIiIiIiIiIiIiMYVnCfEFxY219I6pq6hAU4Dep4xUVlwqWQ4IDYW5ublCOk5V+LRuKu2Zfkkql2LB66aSPJ2NxI9Gc4eHqhE0rE7F+2WKUVNYhu6AMZdUNo77uNRoNyqrr8ekXvfju0w+N2hya7q2moUWw7OflxnFlmjYjIwrs+u5/oriiThDfvGIx/u9fnhcpKyIiIiIiounD4kYiojmkpLxSL+bgIOza1z8wiDMX0nDmQhrCQgIRHOgPf19v+Pt4w9nJ4Z4XbzUaDdo7OlFd24CaugZUVNWgvKpmzO2lUim2rF2OjauXjlqcOBFSqX5enLmRaPZzsLfDiiUJWJ4Sj5q6BmTnFqCguAwjIwq9bWvqGtDc0gZPDzcRMjVtJeVVguXJdpQmmoymljZsfeYH6OsfEMR//urzeGbXFpGyIiIiIiIiIiIiImOJCQ+Cs6M9Orp6tLHUtKuTLm7UnbkxMjzMoPwmS61W42LaVUEsLjYKDgY0D9QtWFKpWNxINNvJZDJEhQYgKjQAvX0DyCmqQHZBmd7sbgDQ3tWDmoYWBPpyLG8i1Go1SiprBTE/L3eRsqG5RqPR4Nu//CPOXbkpiM+PCMSn//cjyOWG3U9FRERERERkCljcSEQ0hxSXVgiW/Xy84evlgabW9lG3Ly2vQuldBS22Ntbw8/GCra0NzM3MIJPJoFQqoVAq0dPbh9q6RgwMDo4rFz9vTzz9yFaDZmu82+gzN3Iwj2iukEgkCPDzQYCfDzavXYmC4jJk5xagpq5BsF1hSTmLGydocHBIr1A9NChApGxorhkYGMRDz/0QtQ1NgvhjD23Er370kkhZERERERERERERkTFJpVIsT5iPw6cuaWPXb+Zhx9aNsLO1mfDxikqExY0RYaEG5zgZRaUVaGkTjsOuSIk36Ji6Y6JqDcdDieYSO1trLI2fhyWLY1Df1IbsgjLkFldi5K4ZYgvLaljcOEFVdU3oHxgSxMKDJ1dgTzRR//P3fXj3wAlBzMvdGUf++jPY2ViJlBURERGZFIn01r+5js8BkUljcSMR0RxSVFouWI6JDMPPXvkG8orLkZqRjcLSqnvu39c/gMKS8ntucy8SiQSxUWFYkRyPyLDgUWdbnCzdLqUAixuJ5ioLC3PEzY9G3PxoHD+TiivXbmjXFZSUYfXyZPGSM0FFpRWCmXDlcjkiQoNFzIjmCrVajWde+QWu3sgXxFMWz8c7v/v3e84mTUREREREREQ0FcS6HqHRGHwEY6RB4ybidSuDfkfFy1sikWBJfCy+OpsO5dfXo5VKJT4/egpPP/rwhI+nP3Pj9Bc3KpVKHPpSp1DBww2hwQFf/5wm93xLpTLBvrdmbjSha6WGFGMaWsgp1jVlQ8+rMWB/Q2/sNOQDSGLITGOGfm4Z8LtiyM/L4A/s8T9nEgng6+0JX29P2NvZ4lx6tnZdYXkNNq1KnOD3FpFuAp4hYz35JZWCZS93F7g4OUzdCQ16bYr4nmAItdKw/Q15fRlyboPey27NyngvB45dxL/+9h1BzNrKAof//iv4+XgZcGbV/Te5FzFfm4Y85yLmbdjfiga8Ng19zGLlTZPAv+uJiIhodpudxY1i/nFl+MjSHGTIz4vP9/Qz1efckLxNdQASegM8ul1KI8OCIZMCC6JCsCAqBC1tnbiYeQPpWfkYGBR2pTOErY01libFYVnSIrg4Od5/h0k8bukoMzeq1Orp/Uww5DPA0M8PUT9/xDq3oecV8/NnZgzSzAUxkWGC4sbWtg60tnXAzdVZvKTGa4a8rvOKigVrwoIDYGFhhnu/DkzzBhpRb/wxpCDfoME8Qwe2pm7A4qe//iM+O3pWEAvy98Hn7/0OlpYWgMaA3DUmOtBi8PcaU/38EfG7vEE374h4E8wMuSFk7jD0u7wB+4r6szbRv7MNxWt/REREREREU8LO1gaLYiOQeaNAG0vPzMKi2GjERIWP+zhKpRJl5cIilQgRihuPnb6A+sZmQWzlkgSDi6R1m8ey2SsRRYcGCoobe/oGUNfYCj9vdxGzMh0qlQoFpdWCWEx4oDjJ0JySebMYz/7kN4KYRCLBh6//PyyeF2Z4QSgREREREZEJmZ3FjURENKriMp2BvDDhzFvurk7YuWU1tq5fhvyyOlTXNqCmvgk19Y0TKna0tbGGv48X/Hw9EeTng6iIEJjJp/YjRyqVQiKRCLqeqdW86ZRorvP19oSdrQ16+/q1scKSctMobpwB+vsHUFldJ4jFRE7/TSA097z18SH85i/vC2IO9rb48sM/wM3FSaSsiIiIiIiIiIiIaCptW78MOYVlGBoe0cY+PnAEP//xd2FtZTWuY1RWVUOhUAhikeFhRs3zfmrqGnDi7EVBzMfLA0sSFxl8bJlOw1cWNxKRm4sj3Jwd0NrRrY0VltewuHGcKmsbMTg0LIjFhAeJlA3NFdX1zXj45X8XfOcBgP/9fy/i4fVLRMqKiIiIiIhIPCxuJCKaI9RqNUp0upRG6hQ33mZuZoa4eZGImxcJANBoNGjv7EZNfSOamtswolBgRKGESqWCXC6DmVwOC3NzeHm4wt/XC05OjgZ3HZ0MqVQKlerOLE4qQ2ejIiKTJ5FIEBUegsysHG2soLgMK5YkiJiV6SgsKRcUipuZyREeEiheQjQnnLmYge/89L8FMblcjgNv/gZRY3x3ISIiIiIiIiIiItPn7GiPHZtW4pPDp7Sxru4eHPziBJ5+9OFxHaOopEyw7OLiDBeX6Wt4qFQq8eHeQ4KiQ6lUimd2PwS5XHaPPcdHKpUKlu8eGyWiuSsqNACtmXeNh5ZWY/2yxaLct2Fq8oqF99H4eLrCycFOpGxoLuju7cfWl36J5rZOQfyl3Zvxw+d3iJQVERERERGRuFjcSEQ0R9TWN2JQZ/bFyLCQce0rkUjg6uwIV2dHIHZcO0w8QSOQyWSCATx2KiUiAIiOCBUUNza1tKKjswvOTo7iJWUi8opKBcsRoUEwNzcXKRuaCwpLK7DrpZ9AqVQK4n/5759i7fIkkbIiIiIiIiIiIiKi6bI0YT6y8ktQVFatjaVnZiEqPATxC+8/UFlULLyuHRkeavQcx6LRaPD5V6dQ39gsiG9aswx+Pl5GOQdnbiSi0USHBSD1ruLGrp4+NLV2wMvdRcSsZj6lUomi8hpBbB5nbaQppFSq8Nirv0Z+abUgvm7pIvzx377LgmQiIiKaHIlEtPu2ZxQ+B2Sgnp4esVMwSE9Pj0n/TSG9/yZERDQbFJWUC5bt7Wzh4e4qUjZTg51KiWg0/r7esLG2FsQKdd4TSV9vbx+qaxsEsZjIsDG312g0fN8lg7S0deDBp19Fd0+fIP6T7zyLF5/cLlJWRERERERERERENJ0kEgme2r4RlhbCRnvvf/oZCnQKF0dTXCqcuTEibPqKG0+cvYizF9MFMR8vD2xau8Jo55BKhTcoqVQsbiQiwMPVCU4OtoJYgU7xFOkrr27A0PCIIBYzRnGjQqFEU0s7+voHpiM1moU0Gg1e+Y+/4OTF64J4dKg/9v3xZzAz4zwlREREREQ0d/EvIiKiOaK4rEKwHBkWYtLV+aORyYTFjexUOjEqlRoSiURvUJTI1EmlUkSGBeP6zTxtrKC4DEuTFouY1cxXUFIOjUajXbawMEdYcIBgG6VSicKScmTnFqKyug7WVpbYumk1IqfxZhGaHYaGhrH9G/+Eypp6QXzHljX473/9vkhZERERERERERERkRicHe2xY/MqfPL5SW1MpVLh7+/twbeffwKRYSFj7ltUojtz49hN+4zpTGoajhw7LYhJpVI8s/shyOWyMfaaOJmUMzdO1NDwCNo7uzEyPACFQgmVWgMzuQxmZnJYW1rA2dGe46Nk8iQSCaJDA3D5er42VlBWjTVL4mbdfSHG9P+zd9/xTd3n/sA/kveQvCeWGWZ6sKfZ02YHSDBZbdOQ5nbS3pK72uTmpr339ndD2tDb25GQpm0WhkDCttnTZg/jgdlY3tvytsb5/UExfCUbsGX5SPLn/XrpBec5OjqPJGud73m+T86Nu8JydL9QqFU+7cuSJKG4rBIXs68jO/8OWtv08HB3w/IFMxDLDo/URRv/8hX+9MUeIRYa5I9dH74Dv0f+7oiIiIiISD6nT5/G5MmT5U6jyzIzM598JTvH4kYioj7i2g2xuHHYEOc70GreudFkkjq5Zt9V39CEguJSFBSWoqC4FIUl5WhobILeYGh/vFxclHBzdUWgvxqaiFBo+oWhf78wRIWHwt3dTeZ7QNQ9scMGC8WNRSVlqNPVw0+tkjEr+5ZzTTwBZNjggXB1vf/zobSsAhezcnE1Lx/Nza3t12lsasbWHWl46bnlGNhf06v5kuMymUz49k/eRuaFLCE+YXQc/vbbX1h8vhMREREREREREZHzmzo+AYUl5Th+5nJ7TK/X4/ebPsW3X16N0fEjOtwu/8YtYXn4MNsWN0qShN3ph7Hv4DGLdS89twyafhE9uj/z46VGo7FHb9/RtbS2QVtchoKiUhQUl6GgqAzlVdWQHjNs7Onhdn9MNDIU0X+/hAT6s+CRHE7sELG4sapGh4qqWoQGB8iYlf3S6w24dkvsbhk/dBAAoKm5BVl5t3Ap5wbKKmuE67S26bFt3zH4eHuhf1R4r+VLjm3noUys/+8PhZinhzu++sO/YwD/joiIiIiI7MYrr7yCpKQk+Pn5yZ3KU6urq0N6errcaViNxY1ERH1ER50bnQ0H8yxJkoRb94pw8txl3LhdgJq6+iduYzSaYDS2obisEsVllThzORfA/dkew0MCkTA8BlPHj0RwoON8cSMaEN0PXl6eaG5uaY9du3Ebk8aNkjEr+1Wnq0dBYYkQGzKwP85dzMLFrFyUlFV0uq3RaELq13vx6ovPISQ40NapkhN4e8OfsHmH+OM6ul84dvzlN/D29pIpKyIiIiIiIiIiIpKTQqHA6iVz0dzSinNX8trjBqMRm/6WioXzZiJ57gy4uDzsZFhVVY3KyirhdoYNGWyzHBsam7Dlqz04f/mqxbrnlidj8vieH4OwmOxVYudGo9GE7PxbOH7mMq7dugfpcZWMHWhp1ePG3SLcuFvUHlP5eGHK2FhMG5+AoAB1T6dMZBORYcFQ+3pD19DUHsu/rWVxYydu3C1EW5tBiHl6uGHrniO4dqsARmPn769GkxGbdx3EqylLEBzob+NMydFdzLmBF3/yK4vPp4//Zz0mj+l4sgYiIiIiIpKHJEkOWSjY1eNh9ojFjUREfYRF58bBg2TKxHZclC7CssnUdwfzWlpbcfZyLo6fuYTi0s6LkLpCkiSUlFehpLwKB06cRdzQQZg+cRRih/RnVy2yew2NTRYFz3W6Jxf79lW5+TcB3H/d19bVo6qmFl/vPQjjU76vtrS04rMvd2LtS8/B19fHlqmSg/vb1t345fviLKUqXx/s+ttGhIcGy5QVERERERERERER2QOlUoFvrEqG0WjExezr7XGTyYQ9+4/gSnYevrFmJaIi73c8yr9xU9jezc0NAwdE2yS3y9l52LxtF3T1DRbrnlk0D7OnTbLJfh8t5gQAk8nxT1zqLl1DI06dy8LJc1eeaoLXrqhvbMb+Exdw4OQFxA0ZgBnjYzFicDSUCnZzJPtVWlGNhqZmIdbappcpG/uXc/0OgPvnVpRWVKNNb8D2tBNPvX1zSys++3o/1q5ZCh9O1kmdKCypwPLX30ZTc6sQ/+U/fgurF82QKSsiIiIiIuqMQqFwyEJBR837USxuJCLqA3S6ehSXlAmx4UOcr7hRqRQHk/pi58ZaXT3Sj57GmUvZaGlts9l+JAnIzr+N7PzbCArww4xJozBz8mi4ufKrBdkfSZKwO/0I2swG75zxfbCnXM29Dm1RCYpLy9Hc0oKI0JDHFjYO7B8FhUKB23e17bHaOh0+37YL33p+Jdzd3XsjbXIwx09fwGvr3xFiSqUSqX/6FRJGDJEpKyIiIiIiIiIiIrInLi4u+HbKEnh6HEDGBbFDYmFxKX71/h/buzhey78hrI8ZNABubm49mk9DYxO2fr0X5y5lWaxTKIA1KxZj+pTxPbrPR3E89H5R49dpx3AuK++xndUepQDg5uYKN1cXKJVKGAxG6A0GGJ6wvSQB2dfvIvv6XQQHqLFo5nhMHDkUChY5kp0xmUzYeTBDKHh2cVFi1IgYGbOyX21temRfv4urebdQU1cPCRKGDtR0en1/tS/GxA1BUVkFrt8ubI/X1NXjix0H8c1nF8LNjedKkKihsRnLX38bxWViV+lvrpyPf/mHFJmyIiIiIqekUN6/9HV8DKiH8LiPPPirmoioD7hx+66wrFQqMXhQf3mSsaG+PFOpJEk4fTEbX+45hOaW1sde193NDZp+EdBERSI6KhKhwYFwc3ODq6srIEnQGwxoaW1FcUk5CgqLUVBUjJLS8k5ndKiqqcNXaceReTEH31iZhP5R4ba4i0TddjU33+J9cOzIOERHRcqTkJ2rqa1DcWk5Avz9UFZeCQAICQ60uJ5a5YvR8cMxZmQsAvz9oNfr8dfNX6OwuLT9OsWl5di2az9SVixih1cS3Lh9DytfXQ+93iDEf/vLf0Ly7KkyZUVERERERERERET2SKlU4sUVCxDor8LeI5nCGOCDLo7nL13F9fw8YbthQwb3WA7VtXU4dfo8TmSeQ0Njk8V6Ly9PvPTsUowZGdtj++yI5Xjo0xX3OQNJknA+6xq27DqIxuaWTq+nVCrQLzQQmogQREcEIzoiGJGhgR1O0lpX34SCkoq/XyqhLalEXYPl8wsAlTU6/O3rw7iYcwvPL50Jf5VPj903ImtlXMhBSXm1EJs+IQEhQf7yJGTn7haWApKE5pZWSJCgUCgsHitXVxeMGNwfY+KGYKAmAgqFAm1tevzly30ofuSxLiwtx/a0Y3hu8WyOh1I7o9GIF9b9ApfzbgnxWZNG4o+/+BFPliYiIiIismOO2AHRGX5jsLiRiKgPKDLr2hgZHgoPDw+ZsrEd8wPFRlPfmKm0tk6Hz7btRk7+7U6vExIUiGlTxiNu+FCEhwY/1UH1oTED2//f1tYGbXEpLl7Jxulzl9HcYjlgWFpehQ0ffIF50yZg0ZzJ7OJIdqGxsQlph04IMZWvDxbMniZTRvbvxq278PBwR1tbG6L6haG8ohoB/moA92d4HTZ4IMYkxCJmYLTwXuLm5obnVy7Gpk+3oqZW1x7Pv3kbaYeOY+G8mU7xA4qsV1VdiyUv/wDVNXVCfN1rL+B731otU1ZERERERERERERkzxQKBRbNSUT8sEH427Y0FJdVCuvLKipx4bLY2XFAdOdduJ6GyWRC/s3bOJ5xDlk51zo9sSl+xFC8sGoJ/P1UVu3vaViMh/aRzo26+gZ8sX03ruTe6PQ6/SNDMH18LMbFxsD9KTuo+am8kaDqj4ShDycGLq2owYmLeTh9JR8trXqLbbJv3MMvf78ZzyZNxaRRwzj2QbKrqqnD0TNXhFhIoB+mjU+QKSP7V1hagX5hwYgZEImrebehVnu3n98QHhKIsfFDkTB8ELw8xfNq3N3d8PyyediUuhd19Q3t8bybd3HgxDkkzZzUq/eD7Nf6//ojdh/OFGJDB/bD1t+9CXf3nu0qTUREREREPUelUmH9+vWIj4+HSmX7Y33Wqq+vx9WrV7Fp0yZotVq507EKqw6IyHHIVgVv7X6tGczomYGQB523HogID32KrRxv1gHZZyq1auCq69tKkoTTF7Lw5c70Drs1KhQKJMQOw4zECRg+JMaqWQLd3d0RMyAaMQOisSx5Hs5dysLxjLNChzbgfrfM/cfPIuvaLQfo4ijn37g1++6jA6TdfH2lHT6BpuZmIbZo/kx4evZSgbcDzuBy4/Y9pB++XxAqSRIC/NQIDQnG2JGxGBk7DD4+3p1u6+PjjRefXYqPPtuG5kdmTT57MQsB/n6YMmHMU2RgxWOmkHM2VAd9bVp70oXUtc/atjY9Vr36E9y4XSDEl86fjg1vruvy7XWbw55s4qB5W/u8WvXalvO7PPUuK59rq76eyfh3Ys13DVnfCx34temwnyFERERERA7IUX9vUTc8/XMd3S8C//y9l5F25DTSj58Wujg2NYkd967duI3N23cjOioS0VGRiAgLsRhPfNSDyT4LCotRoC3GzTv3UFVd0+n1vbw8sXp5MiaOHdlrBW6yj4daqxsP04UrOdj81T40NjVbrHNzdcH4+MGYPi4W/SNDeiBBIDwkAM8lJWLZ7Ak4n30Tx87noqisSrhOc0sbPtlxBJdyb+OFZbPg59v52InVZD0+KhOrX0+dv86fyKrjbb3/epQkCTsPZcJgNLU/bgqFAssXTIerm3vvJCHr8bLuvT5u3StGcXk1fL19oFAqER4ahAmjRmBM3FBEhAY99j1d5euLF1ck4c+pu9HS2tYez7yYA38/NSaNietWTk9NacXft9XPlRXvR1aP41rx+lJY8ZiZDF3e5Pef7sTGj78UYkH+auz609sIVHsDTz1RuxXPl1LG04LlfE+Q9XwB6jKOtxAREZEd+uUvf4kFCxbInUaXxMbGQqPR4Nvf/rbcqViFxY1ERH1AaXmFsBwe2jMDO/ZGqRQPejjcYF4XmEwmfLF9L06dvdTh+gljR2JZ8jwEBfr3+L49PNwxbfJ4TJ00DrfvFiD1qz0WRY73uzhuxssrkzBx9Igez4HoaeTfuI2redeFWOywwRgxdLBMGTmGpuZmJIwYitLySri7u+G55clYvnDeU5+YERwUiDUrFuNvqV8LM0bvP3ISfmoVYofx8e/Lvvsv/4njpy8KsdFxQ/HZ//3nY08qIiIiIiIiIiIiInrAzdUVS+dPw6jYwfj0q/0oLCkDYFnc2GYw4njGWWG7yIgw+Hh7wc3NDS4uLjAYDNDr9aitq0dpeUWn3RnNjYwbhjUrFvdKt8ZHWXZudN7xUEmSkH7kFHamHelw/djYQVidPBUqHy+b7N/D3Q1Tx45A4pjhyMq/h837TkDXIBZYZt+4h/c+2o4fvrwUIYF+NsmD6HHOZ+XjXpE4Vj9p9AhERTzNhNd9k8lkQlnl/cJ1Tw93jBoxGN/7xgoMGxT91LcRGhyIlKXz8OlX6cJ4aNrR0/BX+2JYTP/HbE3O7PjZLPzonf8TYu5urtj2fz/D4P6RMmVFRERERERPKzExUe4UuiUhIUHuFKzG4kYioj6g1KxzY3hosEyZ2FZfGcwzGIz4y+avcDErz2KdWuWLF55dhpFxw22eh0KhQMzA/vjnda8j/dBx7D14TCgoNZlM+Nu2fWhpbcWMSaNtng/Ro1paWrHnwFEh5uXliUXzZsqTkIMwmUwor6xGUGAAggIDAAAJI4Z1ecbp/pp+eGbRPGzbld4ekyQJ23fvh1rli6hIe+7qSrZyOTsfH2/eIcQiw0Ow8y+/ge9jOoISERERERERERERdSS6Xzj+9YevIP/WPRw/fRF/MStu9PISi970BgPuaYu6vT8Pd3dMGjcSM6aMR2R4qCzdZpRm+5QkCZIk9VrnyN4iSRK+3ncIB45mWqzz9fZEysJpGBs7qFdyUSgUGDV8AAb3D8fWtAycy74prK+qrcevP/4aP3x5CSJDg3olJyIAqNM14MDJ80LMX+2LOVPGypSRY6iqqYPB8LAgMcBPhajwrk8QPjA6EsvmT8NXacfaY5Ik4cu9R/Ct5xajXzdukxzfL//vM4uJ2D/8z3WYPj5epoyIiIiIiKgrfH195U6hW1Sq3p2AzRbYh52IqA8oLesbnRvNOz45Y+dGo9GIDz/Z2mFh48Sxo/DmGz/olcLGR7m4uGDRgtn4lx//g0XBkiQBqbsO43DGxU62JrKNg8dOQVffIMSSZk+Dr6+PTBk5huqaWuj1eiHW3YL4hNhhmDtjihAzGAz4/MtdqK6p7W6K5MAOnTwjLHt7eWLXX3+DqMgwmTIiIiIiIiIiIiIiR6dQKDB88AC8+vxytLa2Cuu8vXtmUrXI8FCsWbEI//3zn2DNikX3CxtlYj4eCjjfmKgkSfhqb8eFjWNjB+Hn//BcrxU2PsrHyxPfWjEH31m9wKJbpK6hCRv/uhOlf+8GR2RrkiRhz5HTaDMb11s2byrc3d1kysoxlJm9TlW+3vDx7l4H2FGxQzDLrJhUrzfgix0HUFtX3+0cyTEZjUZkXsoVYj/77hq8tHyOTBkRERFRn6FQ8PLgQkQOi8WNRER9QGm5WNwY1mc6Nxo7uaZjMpkk/C11J67m3RDiri4uePWl1fjWC6vg00ODtN0RFRmOf173OmZNm2yxbtveo8i8kC1DVtQX3S0oxPnL4t9bzMBojIofIVNGjsO806/K1wc+VnTUmzZ5PMaNEmehbGpuxmdf7kRTU3O3b5cc06Wr14TlNc8kYUx87xbkExERERERERERkXOqrKq2KPJbsWgexsQPR1CA/1PfjkKhQGR4KCaPG4Xnlifjn3+4Fj/7yeuYMWU8PD09ejjrrjMfDwWcb0w0/cgpHDxmWdj47IIpeHXVPIvCwt42atgA/Oz1Z6EJF7s0NjS14H8/2YWqWhY0ke1l59/B9TtaITYmdggGRUfKlJHjKK2oFpbDQwKtur2Zk8dgdNwQIdbQ2ITPvt6P5pbWTrYiZ3TjbhEam1qE2PdfWiJTNkRERERERI7FVe4EiIjI9syLVZy2c6PSuTs3bt9zAOfMCrY83N3x+ivPY/iQGJmyErm4uOC55Qvh6+ON3emHhXWffX0AKl9vxA/r/ZlUqe/Q6/XYue+QEHN3d8PSpDlQcGaeJzIvhu9u18YHFAoFFs2fiTpdPW7eudcer6quxeav9uAbKc/A1ZU/SfqKi2bFjeMSWHBMREREREREREREPaO0TDy+rVAosHrZgvZj0A2NTdAWl6G8shptej30egOMRiNc3T3g5uoKTw93REaEIioi3K67njl758YLV3KwM+2IEFMoFHhp6UxMHjVUpqwsqXy8sO7lpfjD5jTc0pa2x2t1jfj9Z3vwz99ZBXc3+/07IsfW2NSMfUfPCDFfby8smDFBpowcS2lFlbAcFmxdcaNCocDSedNQp2vEHW1xe7yiqgapuw7ipRVJHA/tIy7m3BSWI8OCEBYcIFM2RERERETUFefOnZM7Bas5+n1g50Yioj6gp4tV7JVSKRYuOdMspddu3MbhE+IAhZubG77/2st2U9j4wP1ipll4ZtF8IS5JEj7dvh8N7NZGNnT05BlU19YJsbkzEuHvp5YpI8dSWiYWw/dEp98HRc/mhfUFhcX4eu9BSJJk9T7I/jU0NiH/1l0hNjaBXRuJiIiIiIiIiIioZ5iPh4YEBwnFJL4+3hgxZCBmThmH+TMmY9HcaVi6YCYWzp2OeTOnYNrkcRjUX2PXhY2Ac3durKtvwOav9lnE7a2w8QEvT3d87/lk9I8Uxz9KK2uw67Bjn0xG9i3t2Fk0tYjd4RbPmQIvO+gu6wjKKmuEZWs7NwL3x0NTls5FqFkh211tCXYeOMnx0D7iUq5Y3Dg2dkgn1yQiIiIiInujUqmgUqnkTsMqjn4fWNxIROTkGhoa0WRWTBYe5qSdG13MOzc6xwHilpZWfLp1txBzcXHB699ag8ED+8uU1ZMtmDMdC2ZPE2L1jU3YuvtwJ1sQWae4pAwZ5y4JMU2/CEwYkyBTRo6nrMK802/PFMN7eLjjhWeXQq3yFeLZeddx+ERmj+yD7FtW7nVh4NbFxQUjRwyWMSMiIiIiIiIiIiJyJqVl5cKy+YR7zsLFxfI0H2cYE5UkCZu370Wj2bj2swum2GVh4wOeHu74/gsLEREiFjQdOX0FtwpKZMqKnNn121pczb8txGIHD8CIwfZ73oA9aWxqRn1DkxDrieJGAPD09MALyxfA18dbiGfl3cTxM5d7ZB9k3y7l3BCWx8RxLJSIiIiIiOhpsbiRiMjJmc9SCgBhIc7auVH8WDOanGOW0u17Dlp0olu1NBmxw+x/lrfli+YjIXaYEDuflY/LuTc62YKoe4xGI3bsO2hWPKXEsuQ5Hc5iTJYaG5tQ39AoxHqy069a5YsXn10GDw93IX4i8zwqKqt7bD9kny5evSYsjxgyEF5enjJlQ0RERERERERERM7GfEw0rAePb9sTZ+3ceP5yNq7k5AuxMSMGYfYk+5/A0sfLE2tXzYPrIxPxSgA+3XEEbXqDfImR02ltbcPuw+KkoZ4e7lg0e5JMGTme0gpxTNLNzRWB/uoeu31/PxVefGaBRRfg42cuo61N32P7IfsjSRIu5oidG1ncSERERL1GoQAUSl4UCrmfCSKyAs/0JiJycqXlYhcuX18f+Pr6yJSNbZkP5plMJpky6TnXbtzGyTMXhdiQmAGYkThBpoy6RqFQ4IVnl1kUsGzeeQgNZjOvElnj1JkLKKuoEmIzEyciJDhIpowcj/nnhZubKwID/Ht0H2GhwVi9fJHF+3VVTU2P7ofsj3lx49iE4TJlQkRERERERERERM6otEwsbnTWzo0dFTc6+phoXX0DtuxIF2K+3p5IWThVpoy6LjwkAEtmjRNi5dV12HX4rEwZkTM6cPI8dGYTlSbPnGjRKZA6V2Y24WpokH+PT5QbERaMlckzhZjRaERLa1uP7ofsy93CUtTqGoTY2Dj7n7CciIiIiIjIXrC4kYjIyZkP5IWFOGehj8FggLawSO40elSbXo9Pv9wtxDzc3fHy6mccqhOdn1qF1c8sEmL1DU3YvveYTBmRs6msqsaxDHFwOCwkCFMnjetkC+pIWYVY3BgaHGST95qYgdFQq3yFmFLhOO9p1D2XruYJy2MSRsiUCRERERERERERETkj82Pc4WHOWdx44dIVi5irq6sMmfScr/YcRKPZpKgpC6dB5eMlU0bdM3fySAzoFyrEjpy+gqKyqk62IHp6dwtLcP6q2N00JrofRo1gZ7iuMO/cGG6j82c0kWEWMRcXjoc6M/OujUEBamginPO7CBERERERkS3wVzMRkZMrLe8bs5QeO5GJyirxQHR0VD+ZsukZFy7noLqmTog9s3g+goMCZcqo+yaOHYWE2GFC7OyVPNTU1suUETkLSZKwY98hGI0PZyVWKBRYljwXLi4uMmbmeCw/L4Jtti+DwSgsu7ryuXJmLS2tyLl+W4ixcyMRERERERERERH1JMvOjaGdXNNx1dTU4tDRE0IsLDQEarVKpoysV1ffgPOXc4TYmBGDMDZ2kEwZdZ9SqcTLS2fC9ZHxKQnAsXPZ8iVFTkGvN2DngQwh5ubqiiVzp0ChUMiUlWOyLG60zbkXj45dP+DiQBNYU9ddzLkhLI+NHczXJxERERERURc49vRt9siaH6WS1HN5OJS+er+JnoLVB7oUKC03m6U0NATA09yuNfu2Pu+uKC0rx6nT54Tt+kWGY/iwod3IxYr3pB5+Hz+WeV5YjhkQjelTJvToPnqLQqHAC88uw9v/byNaW9sA3C9KO3kuC0vnJXb3RnswQ0dizd+ZtY+Z/X1mn7uYBW1RiRCbMmE0+kWGy5SRPena89VxMbxtXmdGk1jcKBaiOupr29rXhxXbG/VW7NbQ/W0BPM3zlX3tOgwGcT+jYwcD1nbslCwHhomoB1j7ndaat3GrvuY46ucHINv3O6ufazkfc/v7XkpEREREfYPksOO5cv1Yc2AOO+7voL+PrXm8/76txTHu8JBe+O3ae4+3JEnYtW8/9HrxWOuipLm9lsMj2fTYLWWcvQST6eGxXjdXF6QsnNpjt9/bwkMCMG/KSKSdvNQeO3f1Jp6ZNxnenh4yZGTl36jSiskprTqEL+f7qBWJS1aOeyg63vexs1dQXVePR5/PuVPHIcBfbd3+7ELvFfwZDAZUVtc+DCiAsJBAm3xWGIxGi5gw2as1Y2RW5WvlfbXqK6WV43rG1u7v2pqx1KdkXtw4Jm4wJMnouGMX1o7jykTeglKZXtdW32e53o+s5KivLTlZ8z6scNAJyx16LJSIiJxJQ0MDAKC2thb+/v7w9fWVOSP7xOJGIiInZzFLaZhzdW40mUzYsTtNGPRycXHB8sXJUDrwzHd3tcUoKBQLtubNmurQ98lPrcLEsaNwIvNce+zUhatYOGsSu7ZRt9TW6XDwmDhLaaC/H2ZPmyxTRo7LYDCgsqpGiIXZsHOj0fi44kZyNhevXhOWhw6KhsrXR6ZsiIiIiIiIiIiIyBmVlpULy87WufFyVjZu37knxCaMG43+0RqZMrKe0WjCidMXhdj4+MFQ+XjJlFHPmD4+FvszLsNkun8ydZvegDNXrmP2pASZMyNHVFxWiYwLYvfPqIgQTBw9QqaMHFdFdW376/KBsGBbdW60LG7keKjzkiQJF7MtixuJiIiIiKjvycvLQ0ZGBjIyMqDVaqHVaju8nlqthp+fHzQaDaKiopCcnIwpU6b0crb2hcWNREROrqyio86NziPj9DmUlJYJsemJkxDq4PfzuFnXxkB/PyTEDpMpm54zM3GiUNxY39CEK3k3MS7B8e8b9S5JkrA7/Qja9OIsi0uT58DNzU2mrBxXRWW15WBeiC2LG8UZ4VwcuHCbnsy8uHFswnCZMiEiIiIiIiIiIiJn1NraipraOiFmywn8elt9fQPSDx4RYmq1CvNmz5Apo55xNe86aut0QmzG+FiZsuk5/iofjBo2EJfybrfHTpzPxayJ8TJ3lCJHYzQasfPAKWEMz8VFieXzpzn0pMhyKS2vFpYD/dXw8HC3yb7Mx0KVSiVf/06suKwKFdXi95CxLG4kIiKi3qRQsNsowMeAZLN161acOnUKmZmZ0OkeHuuSHtNFuK6uDnV1de3Fj1u2bAEAxMbGYtGiRUhKSkJUVJRtE7czPNJBROTkSsvNOjc6eNHfo6qqqnH0xCkhFhoSjGmJk2TKqGc0NDbhwuUcITZtyninGKCIjAjD4EH9hdjxs1dkyoYcWVbONdw0m6F43Kg4DOzvuDMUy6m0XCyEDwzwt9lgniRJFrOVurpyzhVndilbLG4cw+JGIiIiIiIiIiIi6kFlZse4ASA8zHk6N+5NP4iWllYhtiR5Pjw8PGTKqGccz7wgLPePDEF0hHOMZZsXaZZV1SL/TpFM2ZCjyriQjdIKsSBvxsRRCAnylychB2f+WNqqayMAGMzGQl1cHP9cD+rcxZybwrLa1xuDNOEyZUNERERERL2hoaEBH330ESZNmoS33noL6enpqKurgyRJ7ReFQvHEy6PXlyQJOTk52LBhA+bPn4+f/OQnKCwslPuu9hr+ciYicnKlZeJgnrPMUipJEnbuTYfB8PCgsEKhwLLFSQ5fJHM5+xr0BkP7souLCxInjpMxo541I3GisHzzbhGqa3WdXJvIUkNDI9IOHRdiKl8fzJ81TaaMHJ9lIbwtuzYaLWIc0HNeer0BWXnigN7YeBY3EhERERERERERUc8pMzvG7ebmhgB/P5my6Vm5efnIy78hxEbGx2LokBiZMuoZzc0tyL95W4jNGB8nUzY9b0j/CIQH+wuxy9fuyJMMOaTK6locOyNOEhwa7I9pExJkysjxlVWKxY3hIbYrbrSY6NXFxWb7IvldyhXHQsfExjjF5OVERERERGSpoaEB7733HiZMmIANGzYIBY3mhYtPo6NtHtxeWloa5s+fj1dffRXXrl17wi05Pv6KIiJyYiaTCWUVYnGjLQtWetPVnFzcKxBnI5g8cRyi+kXKlFHPuVMgztqZEDsMapWvTNn0vNHxI+Dt5SXE7haWypQNOaJ9h46j2XyG4gWz4enp2DMUy6msokpYDguxZXGjySLmwgE9p3Xt5l20trYJsTHxw2TKhoiIiIiIiIiIiJyR+QR+YaHBTlFUIEkSDhw+JsS8vb2QPH+2TBn1nIKiUkjSw2UXpRJjYwfJl1APUygUmJgwRIgVFFd0cm0ikSRJ2HnwlMVEz8vnT+OYWjdJkmTRuTE8JMhm+zMfD+Xz5tzMOzeOjh0sUyZERERERGRLmZmZmDt3LjZt2tRhQeOjzDsydnTpSEddHU+dOoUVK1bg17/+dW/cTdk4dmsrIiJ6rJraOuj1eiEWHhoiUzY968ZNcWZLhUKBqZMndnJtx6ItLBGWYwZGy5SJbbi6umJAdD/k5j88wFtQXIax8UNlzIocxbXrt5BzTZyhOH7EEAwb4jwD3r1NkiSUlvdeIbzhkc60D7g4wUkm1LGLV8UZg/pHRSAwwDlmTCciIiIiIiIiIiL7UFomFo05y3hoS0sLamrrhFhUvwh4mU0i6ogKisTx0MjQQLi7OdcpTP0jxb/DorJqGI1GFjnRE527cg0FReVCbMrYOPQLd473NjnU6RrQYjYZpy07NxrMOje6uHAs1JldMituHBPn2N2ViYiIiIjI0nvvvdde1AhAKGY0L1RUq9WIioqCv78/VCoV/Pz8oFarAQA6nQ6FhYWora1FfX096urqoNPphO0f3LZ5J8cPP/wQGRkZ+Mtf/gJfX+dpmvSAcx0ZJCIigflAHgCE2nD2ud4UGRGGqzl57cuSJCH94BGsXL74qVs52yO93oBis+ct2gm6UZrTREUKxY3a4vLHXJvovpaWVuw5cFSIeXl6YOHcmfIk5CTqdPVoMeuEGR5mu8FRo8myc6OrK3+WOKuL2WJx49iE4TJlQkRERERERERERM6qtFwcZwoPC5Upk57l5eWFAf01uHtP2x67fuM2Lly6gvFjR8uXWA8wn+w1OsJ2ky7KJTpCHGsxGI0orqiBJtz57iv1nNq6ehw8dV6IBfipMHvKGJkycg5llTXCsqeHO9QqH5vtz6JzIyd6dVrlVbUoLBUnEh4bx86NRERERETOorCwEOvWrUNubm57p0bgYUGjWq1GfHw8EhMTkZiYiNjY2G7tJzc3FxkZGcjIyEB2dnZ7weOjXSElSUJ2djbmzp2LjRs3YvLkyT1wD+0HzyImInJipeVikVxggD88PDxkyqZnTRg3BlnZeSgpLWuPXc3JQ7SmHyaMc9wD+0UlZTA9UvijUCig6RchY0a2ER0lFmwWFJcJX/qIOnLg6EnUNzQKseS5M+Dj4y1TRo5PkiScv5wtxDw9PaBW2W5WF6PZTKUAZyt1ZpfMOjeOiR8mUyZERERERERERETkrMwnfA0LcZ7isWWLkvDBx58IkxTu238YkRHhiIwIlzEz69wzL26MdL6OdN5eHggOUKGypr49pi2pZHEjdUqSJOw+nIm2NoMQXzZ/KtycrLNpb6pvaMKxM5eFWHhIoE3PTTCaxPFQV1d2bHVWl3NvCctenh4YNlAjUzZERETUdykBBc+/A/gYUM+qr6/HypUrUV9f336O+4OixqSkJLz++uvdLmY0Fxsbi9jYWKxduxYAoNVqsXnzZmzdulUodASAuro6vPLKK/j444+dqsCRr2AiIidWViHODhYe6jyDQq6urnhuxVJ4eorFmmkHjqC4pFSmrKxXUCQO5IUGB1ncR2dg3o2yqbkVVbW6Tq5NBNy5p8WFKzlCbPDA/hgZxy5w3WUwGPD13oM4efqCEA8PDbbtYF6HxY0c0HNWpeVVwnJEGE/aICIiIiIiIiIiop5lXtwYHuY8Y6KBgQF4ZslCIWY0GrFl+w40NzfLlJV1mptbUFFVLcScsXMjAGjCxb/FgpKKTq5JBFy9dhs37xYJsbHxQzFQ43yTIfeW0vIqfLh5J4rLxHNnIm08XmXZuZFjoc6q6ZHJBwDAzdWl/WRnIiIiIiJyXPX19Vi1alV7YSEAqFQqrF27FufOncPGjRt7rLCxIxqNBm+88QbOnj2L999/H7Gxse2/NR4UWb7yyiu4du3aE27JcbC4kYjIiVnMUhrqXINCzjiYV2J2UN28w6GzCAzwg4+32G2vxKwAhugBvV6PnWmHhJi7mxuWJM1mt89uampqxt9Sv8aVbMsfNqNsXDBqMIjFjQqFAkolf5Y4q2Ex/YXlwyfPyZQJEREREREREREROSuLCV/DQmXKxDaGDxuCxMkThFhtrQ5f79rnkAUUVTW1wrJCoUBESKA8ydhYVHiQsFxZw8leqWONTc3Yd/SMEFP5emPB9PEyZeT48m8V4M9b90BX3yTEPT3cMS5hmE33bT4e6uLCsVBnNT5+iLCsa2jCyQs5nVybiIiIiIgcxTe/+U0UFBS0FxImJyfj7NmzWL9+PVQqVa/mkpycjO3bt+PPf/4zfH19hS6SK1euRFFR0ZNvxAHwlzMRkRMrLTfv3OhcxY2A8w3mtba1Cctqla9MmdiWQqGwuG9tbXqZsiF7d+TkadSYdfacNzMR/n5qmTJybBWV1fjwky0oKBQ7xSoUCiyYPRWjE0bYdP9GkziY5+rqatP9kbwWz5smLO87kgG93iBTNkREREREREREROSMSsvKheXwUOfp3PjA3FnTEa3pJ8Tyb9xCxmnHm1CuzewYsbubK9xcnbOrmbenu7Cs1xs7uSb1dfuOnkazWQe4xbMnw9PTQ6aMHJckSci4cBWbdx1CW5v4fhPgp8LaNUsQFOBn0xyMJrPOjSxudFpRESEYGzdYiO0+fFqmbIiIiIiIqCe8+uqryM3NhUKhgEqlwsaNG/H+++/LnRYSExNx7tw5rF69ur3A0WQyYeXKlXKn1iP4y5mIyImVloudG51xIA9wrsE8g9lgnqub8xb9uJndN/OBTCIAKCouRea5y0IsOioCE8aOlCchB3frTgE++uxLi2JRd3c3rFmxCIkTx9q8G6bRyMG8vmTJvOnCcm1dPU6ZvaaJiIiIiIiIiMieKay89EEKRfcvjpq3QgGFFRdr/84sxkTDQruwvUy6+Pi6uLri2RXL4O3tJaR96OgJ3CvQdnHnknUXyYqLyQS92WSvzlrYCABuZhM86g0GWP++2oWLQmnlxYr3BaVL9y/W5i3bY9a9xyr/thbZ1+8IecQNHYThgwfi/ql9znyxktljaTSZsOtQBvafOAcJkvDU9o8Kx2svLENwUABs/ZlrNJpN9upi9j5nzfuoNRerWfPacrHqIhn13b7AZLTpZcnsicKjtPvwGUhGA2AyQqF06fbF+vdwa95HrfgrsfJ7oXXfKWV8P7Pyu3S3L3KyOne5Pj+seC+DAz9fVpHx94NV++6jZPuuwOeBiJxDeno6Tp06BYVCAY1Gg0OHDiEpKUnutATvvPMOtm3b1l7gqNPp8Pbbb8udltV64CgBOQdrvwR2/yJJ3b8Q0eNZDuSFyHcwxYY/5F1cXPDsM0vh4+MtxLs1mCfXQWST6f5gnkEs8HMzP8jtRFzNBioNRs5UanuOddDHaDRix75Dwme+i4sSy5LnwtYFePbB2udLPMB67uJVfPblLrSYzfqqVvni2y88i2FDYh65vu2++xmN4vuci1LZc7cv+4CeTCSjFRfbPmZRkWEYmzBciO06cKJn7rdMA3LUDTIOoBL1ij77+dMH7zNg/fNt8Vg4+ONBREREREQks4aGRjQ2NgkxZ53wVa1WYdUzS4QxEpPJhC+/3o2GhkYZM+sak1lHM6XSecd8zO+beTc3opaWVuw+dEqIeXl6YOGsSTJl5LiaW1rxyfZ0XMzOt1g3OnYIXl6ZBG8vz17JxWAQz31wceLzPggWxY037xUj/3ahTNkQERFRnyT3Od/2dCGy0ptvvtle2Lht2zaoVCq5U+pQXFxce36SJCE1NRXXrl2TOy2r8CxBIiInVlrWQXGjk1KrVVi1fLHDD+aZz9jnzAV/5gf0LWYrpD7v5OnzKK+sEmKzpk5CcFCgTBk5JpPJhH0Hj2HPgaMWJwz0iwjDay+v7tXPBw7m9T1L5ovdG3f3VHEjERERERERERER9XmlZeUWsfudG51TzKCBmDktUYjV1zdg247dDjNBtHk3Q4PBeQv+zMdEzO870YGT51BvVqCdPHMSfM0mdqbHq6qpw6YvduFuYYnFunnTxmP5gulw7cXXn3kh8/3JXslZjY0bjMjQICG2+8hZmbIhIiIiIqLueu+996DT6aBWq/HnP//ZbgsbH4iLi8PHH38MtVoNAPj5z38uc0bW4S9nIiInVlpeKSw76yylDwwaOAAzp00RYvX1DTh64lQnW9gfNzfxgLpeb+jkmo7P/L5xMI8eVVFZheOZ54RYeGgwEieOlSkjx9Ta2oYvtu3GmQtXLNbFDhuMbz2/EiqVb6/m1NTcIiybd3El57N0/gxh+cadAuTfvCtPMkRERERERERERORUSsvFyV69vb3g6+sjUza9Y8a0KYjqFyHE7twtwL0CrUwZdY15gZHe4LzjoW0WxY0cE6GH7hQU48JVscvg4AH9MHJEjEwZOaY7BcXYtHkXqmrrhLibqytWL56DaRNGCZNk25rBYEBphTiBL8dDnZtCocDi2ROE2O4jZ2TKhoiIiIiIuqO+vh4ffvghFAoF/vKXvyAqKkrulJ5KXFwc3n//fUiShJycHOzfv1/ulLqNxY1ERE7KYDCgsqpaiDl7caPBYEBVdY1FvKOYvfJwdxOW6x2o62RXSJJkcd883FncSPeZTCbs2HcIRuPDGS2VSgWWL5zHLn9dUFunw0efbcWN23ct1s2YMgHPLV8INzc3yw1txGg04tips9iZdkiI8zl1fmMThiMyXPwOsusguzcSERERERERERGR9cw7N4aHOm/XxgcuXbmKouJSi7jJ5BgdEL29PIVlvcEIXUNTJ9d2bNW19cKyl6eHTJmQvdHrDdh1SJyk2d3dFUvmJPZqIZ6ju3g1H598lY7mllYhrvLxxiurFyN26MBezaegqBR//PRrXLt5T4izuNH5LZk9SVg+dTEPVTU6mbIhIiIiIqKuSk1NBQAkJSVhxIgRMmfTNYmJiVi9ejUkScK+ffvkTqfbWNxIROSkKiqrIUmSEAsLDZYpG9traWnBZ6nbcDUnz2LduNEjZcioe8JCgoRlbVGxTJnYVm2dDg2NYnGj+X2nvuvshSsoNBuUT5wwFhHhzn9CQk/RFpXgw79tQbnZrKAuLi5YuWQB5syY0qsDoyWl5fjwb1tw5OQZoWgVAEKDA3stD5KHQqHA4rnThNjuAyxuJCIiIiIiIiIiIuuVVVQKy+FhzjvZqyRJOHz0BHbtTbcYBx4yeCAGDugvU2ZdExzobzHha0FJZSfXdmzm96tfGMdD6b4jmRdRXSsWPs2bOh7+fiqZMnIsJpMJ+4+fxc6DJy0KuyNCg/Da88sQGdZ758e0temx70gmPt6yB5XVtRbrB2oiey0XksfcKaOEAnaTyYR9x8/LmBEREREREXXF3r17oVAo8Mtf/lLuVLrlnXfegUqlQlpamtypdBuLG4mInFRpeYWwrFQqERzknAUkdXU6/PlvX+DO3QIhrlQqsWxxEuLjHGcGhf5R4kHt0vJKtLS2dnJtx1VQKBZtenl6ICTQT6ZsyJ7U1Nbh0PFMIRYU4I+ZUyfKlJHjuZqbj79u/gqNTeIsx95eXvjmmhUYGTe813IxGAw4dDwTH36yBaXllicmBAcFYMHsaR1sSc5m6fwZwvKpc1dQ1cHgLhEREREREREREVFXlJaJY6Lhoc5Z3Gg0GvH1rr04firTYt2ohDiseXaFw3R7UyqViIoIE2JaJyxuNEkSCkvF+xUd4Zx/n9Q1xWWVyLyYLcSiI8MwYZTjnNcgp7Y2PVJ3HUbGhWyLdcNj+uOV5xZDrfLptXxu3yvCHz7ZjjOXciwKz11cXDBv2gQkDI/ptXxIHl6eHpg7ZZQQ233krEzZEBERERFRV2i1WuTm5mLt2rXw9fWVO51uW79+PQDgwIEDMmfSPa5yJ0BERLZhXkQSGhIEFxcXmbKxnbKycnyaug319Q1C3N3dDc+tWIYhgwfJlFn39IsIhVKpgMl0/6C3JEkoLC7F4IGOMdPq0zIvbtREhjrMgCvZjiRJ2J1+BHqDQYgvTZ4DNze3TraiByRJwrFTZ3H0lOUgSUhwIF5YtRQB/r1XRKwtKsGOfYdQWVVjsU6pVCBx4ljMmjoRrq78SdIXzJ02AV6eHmhuuV+wbzQase/IKby0arHMmREREREREREREZEjKy0rF5bDw0JlysR2WlpasGXbDty+e89i3YypkzF75jSHG2fT9IvArXuF7csFJRWPubZjqqiqQ0ubXohFR/ReJzmyT0ajETv2nxCK4FyULlg23/Fex3LQ1Tfi8x0HUFpRbbFu6viRmDdtfK89ji0trThw4hwuXL3W4fp+4SFYvmA6QoOdcxJysrRkziShoDH9xAW0tenh7s5zHYiIiMjGFMr7l76OjwF1U3p6OhQKBVJSUuROxSqLFi3Cv//7v2Pz5s2YP3++3Ol0GV/BREROqi/MUnr7zl38+ZMvLAobfX198MrLzztcYSMAuLu5WTxX5oWAzsD8PkVHOt9AM3Xd1dx83DLrwBoSFAh3NzeLWS5JZDAYsH33/g4LG2MGRuPVF5/rtcLGtrY2pB06jj9/tq3DwsawkCCsfek5zJuZyMLGPsTLyxPzposdWHfvPy5TNkREREREREREROQsSsvNxkTDnGtMVKerx8effGFR2KhQKLB00QLMmTXdIQui+kdFCMsFpc7XubHArBuln683/HqxmxzZpwtX81FWKRbmeXt5IO/mPRSVVsBkMsmUmf0rLqvEh5t3WRQ2KpVKLJ8/HfOnT+i198Prtwvw+0+2d1jY6OrqggUzJuHVNUtZ2NjHLJkljoXqGppw4rxlh1EiIiIiInqyjIwMDBs2DO+++26v7Euj0SAqKsrm+7IllUqFBQsWIDvbMX+H8GxiIiInZd650dmKG69czcGO3WkWB/eDgwLx0ppn4d+L3cl6WnS/cBSXPpxl9vZdLeZMnyJjRj3LaDTibkGREIuODJMpG7InV3PzLWIVVdX44G+p8PbyxKABGgzqH42YgdHwU6tkyNA+NTQ0YvNXe1FYXGqxbsKYBCycNxNKZe/MaXLnnhY70w6jplZnsc7FRYkZUyZg2uRxTtlJmJ5syfwZ2HXgRPty2tFMzlZKREREREREREREVjGf8DUsxHk645WXV+DTzV9CV18vxN3c3PDcM0sxdEiMTJlZL7pfuLBcq2tEWVUtwoL85UnIBvLviuOhmgjnGq+n7rldYDmxcX1jEw6dOo9DEuDl6YGBmgjE9O+HmOhI+PtxTBQAcm/cwVfpJ6DXG4S4l6cHUpbMxQBNRCdb9qym5hakHT2DrGu3OlzfPyocy+ZPR1CA456vQt0XERqICQlDce7q9fbYrsOnMTdxjIxZEREREZEjy8nJQWpqKrKzs6HVaqHT3T8vU61WIz4+HsnJyQ7fbbAjOp0O69atAwBotVqb7y87OxsLFy60+X56w7Rp03DgwAE0NDTA19dX7nS6hMWNREROyllnKZUkCSdOncbhYyct1kVr+uH551bAy8tLhsx6zgBNJE5fyGpfzsrOQ0NjI3x9nGMmz6yca2hsahJi/aPCO7k29SWqx3yRbmpuQXbeDWTn3QAABAX4I2ZgNAb112Bg/yh4eHj0Vpp2pay8Ep9v2406nXhig0KhQPLcGZg0blSv5NHS0ooDR0/hwpWcDtf3iwjD8oVzERoS1Cv5kH1aMm+6sKyrb8C5yzmYOnG0PAkRERERERERERGRwystLxeWw8NCZcqkZ925ew+pX+5AS2uLEPfx9sYLKavQL8Kxx9bCQoLg4+2Fxqbm9tjJC3lYtcA5JnxtamnF+eybQiwm2rGfM+oZA6LCce3WvU7XN7e0IvfGXeTeuAsACPRXI6Z/JAZFR2JgVAQ8PfvWmKgkSTh5LguHTl2wWBcUoMYLzyzotULC3Ot3sOdwJhqbmwGIHSLd3d0wb9oETBg1wiG76VLPWTJ7olDcmHEhV8ZsiIiIiMhRZWRkYMOGDcjJuX8+ZmJiIhYuXAiNRoO6ujrk5ORg3759yMjIwFtvvYWUlBSsX78earVa5sx7xoYNG9oLOXuDTqdDdHR0r+3PljQaDQCgtraWxY1ERGQfyiuqhGVnmKXUZDJhT9oBXLiUZbEubsQwrFi2CK6ujv/RNiZ+OL7cdQAGoxEAYDAakXH2IhbMnv6ELR3D8YyzwvKg6AgEc+ZCAjBn+mQ0t7Qg/+ZtmEzSY69bVVOLqppanL2YBaVSgX4R4YgZcL+rY7+IsF7rVCinG7fuYuvONLS16YW4h4c7nl2ahCExA3slj+s372D3/qPQ1TdYrHN1dcGc6ZMxefzoPvGc0OOFhwbBT+2LOt3DvxVdQ6OMGREREREREREREZEjkyQJZeWVQswZJny9mp2Lr3fvg/HvY4UPBAUG4qU1zyIgwB+QHj+OYu+USiWmjB+Fg8dPt8cyr+Rj6ewJcHdz/PHeM1euQ294+PwplUpMGjlUxozIXkwaEwcPd3dcyrmOwtIKmEymx16/ulaH6lodzl25BoVCgX7hwYiJ7odB0ZGIigiBi4tLL2Xe+wwGA3YdzMCVvJsW6wZqIrB6yRx4eXnaPI+GxibsPZLZXnBqLqZ/PyydN41dNgkA4OXpLiwrlSx2JSIiIqKu+dGPfoT09HQAQFxcHH7xi18gLi7O4nrvvPMOPvzwQ2zYsAGpqanYt28f/vKXv3R4XUfyoFtlb3tQFOjooqKiIEkS6uvrn3xlO+P4RwSJiKhDzta5sa2tDVu/2okbN+9YrJsyaTwWzJ3lNDPgqXx9MHbkCJy9lN0eO5F5HvNmTnX44qCSsgrkmz2H0yf2Tmc5sn++vj5IWbEYLS2tuFtQiFt3C3D7rhZVNbWP3c5kkqAtKoG2qARHT52Bh7s7BvaPwqD+GsQMjEagv5/TvD8A90/WOHsxC2mHTkAyO3nB30+FF1Yt7ZUOiU1NzUg/chJXsq91uL6/JhLLkucgKDDA5rmQYygqKRcKGwFg+OAB8iRDREREREREREREDq+mtg56vTgBYHio43ZulCQJpzLP4OCR4xbrNFH98PxzK+Dt7S1DZrYxfdJYobixuaUN53NuInH0cBmzsp4kSTh+XuzUNXr4QPipfGTKiOyJQqHAmPihGBM/FK2tbbhbWIpb94pwu6AYldW1j91WkiQUllSgsKQCx85choe7GwZERWBQdARi+vdDUIDzjIk2Nbdg865DKCgqs1g3Nn4oFs+ZYvPCTkmSkJV3E2nHzqC5pdVivaeHO5JmTsLouKFO87iT9a5cE8+HGTl8kEyZEBEREZEjeuWVV5CRkQHgfrfGjz/++LHXf+2115CYmIiVK1dCp9Nh5cqV+Pjjj5GYmNgb6drEunXrZNmvSuUcE9b4+/vLnUK32Ulxo+Lvl57ioDPUWXugw6qZ+ax9/Lu/b2sO8Jif0E9ED5WWmc1SGhIESI+f9c9eNTQ04vMt21BcIh64VigUSJo3C5Mnjn8kauX7gjWPkVXbinnPmDRGKG6sqq5Bbv5NxI9w7Bk9T2SeE5Z9vb0wJm6ITNmQvfL09MDwoTEYPjQGAFBbp8Ptu1rculuAO/e0aGpueez2rW1tuHbjNq7duA0A8FOrEDMgGoMGaDCovwbe3l5dS8iOvm8YjUakHTqBc5euWqzT9AvHmhWL4ePz4MQG2+Wdm38Te/YfRWNTs8U6d3c3zJuZiAljEmQYyLP2M8CK7a39O1FYMQDrasWstNZ+N+jC/b6cd0tYVqt8MUDTD93/LSLTa1Nh5UQD1jzm1u6byJlZ/T4s28ZWkvNYkIOy+rudNdvbz/dKIiIiIqLeI9dvDwf9/i1rcYS1+7Zi+27eb/PJXgEgLDQIjvj8m0wm7Nt/COcuXLJYN2LYUKxcvhhubm49szNrjwubjE++zlNsGxKgQuzQgci9/rAQ5Pi5XEwZNcyhC4Xy7xajvLpOiM2YEG/F69uKx8Lal4I1Yxew4u8E1h4Lt2bc3orddvEYvoenJ4YNHoBhf58Isk7XgNsFRe3Fjk8cE9UbkH9Hi/w7WgAS1L4+iOl/v6vjoOhI+HR1TFQOHRyrq6iqwec7DqKmTuwyoVAosGD6BEweG9cz7xGPeb7qdA3YfegUbtzRPriysH5YTDSWzJ0Kla8MRcsOet4PlNYVoyrcrHmsrXjMTIYuXT0r/66wPGp4DLr7nqZQOOhzbfV7uEwc9ruHlY+3w95vIrKNHj5vyo7OuXN6CgXf0wGHfwx+9KMftRc2xsXFPbGw8YG4uDhs3LixvSjwlVdewcGDBx2yE+GHH34IrVb75CvaQGFhoSz77WlardZhj6vZSXEjERH1NGfp3FhZWYXPUrehplYcAHJ1dcHKZYsRO2KYTJnZ1sDofoiKCEPhIwWdB46eRNzwIQ77paO+vgGnz4sDsonj4uHmyq8j9Hj+fmqMHRWHsaPiIEkSSkrLcfueFrfuFKCgqBhG4+MP6tfp6nExKwcXs3KgUCgQHhqMgf2jEBwYiAB/NQID/KHy9bHbzqhtbW0oLa9EcWk5cvNvoqCwxOI6CbFDsXzhXLja+PXU0NCIvQePITf/VofrYwZqsDRpDvz91DbNgxzTlZzrwvKoWMf9TCMiIiIiIiIiIiL5lZaJ46EB/n7w8PCQKZvu0+v1+PKrXci/cdNi3aTx45A0f7bdjmFYa8aksUJxo7a0Etk3CpAwtL+MWXWfSZKw7/hFIRYeEoDB/SNkyogciZ/aF2Pih2FM/DBIkoTS8ircLijGrXtFKCguhcHw+IJRXUMjLuVcx6W/j8eEBQciKECNQH81AvxUCPBTIdBPDbXKfsZFJUlCna4BxeWVKC6rQnFZJbQl5dDrxYIyd3dXrEqehWEx0TbP5+LVfOw/fhatbW0W6729PLFw9hTEDxvEMS6y0NrahrxbBUJs1IgYmbIhIiIiIkeSmpqK9PT09uWNGzd2afvk5GQkJSW138aDAkdHotPpsGHDBln2rVarUVBQ8OQrOoAHxaGO2ImS1QRERE6oubkFdTpxFrvwUMcrbizQFuKLrV+h2WxGQi8vTzz/3ApEa6Jkysz2FAoFZkwei8+/2tceu3HrLjLOXsTUSeNkzKz7Nn+1By0tre3LCgUwbUKCjBmRI1IoFIiMCENkRBimTR4PvV6Pe9pi3LpbgNt3C1BWUfXY7SVJQklZBUrMTnhwcVHCX61GYIAfAvz9EOh//98Afz8E+Kl6bjbkJ9Dr9SirqEJxaRmKS8tRXFqGisqax3arnj1tEmYkTrDpAJokSbiam499h46jubnVYr2npweSZk/D6IQRHMijTmXlisWNI2MduxsxERERERERERERyctystdQmTLpvsbGRny+ZTuKii0nNkyaNxuTJ4536uPu8cMGIdBfjepaXXvsi70nEKMJh7eX4xWqHj+fg5sF4nM5fXwPdZmjPkWhUCAiLBgRYcGYOmEk9HoDCopKcaugCLfvFaP0CWOiAFBWWY2yymqLuIvSBf5q34cFj38vfgz0V8Nf7Qt3d9uMi0qShPqGJhSXVT5yqUBTB2OPj1KrvPHCsvkIDw2ySV4P1NTqsPPASdzRFne4Pn7YICycPcUxOmKSLHJv3rMoQh45bKBM2RARERGRI3m0qC8pKalbXRdff/319uJGrVaLDz/8EK+99lqP5Whr69atg1p9v6mGTqd7wrV7VlRUFHJzc3t1n7Zy9epVAPfvk6NhcSMRkRMqq6i0iDla58bcvHxs37nH4sBfgL8fXkxZheBg2x64tgcTRsdiz6ETqNM1tMe27UzDiKExCAzwly+xbrh4JRuXsnKE2Ni4oQgK8JMpI3IWbm5uGDyoPwYPuj+Db0NDY3tXx9v3tKhvaHyq2zEaTaiqqUVVTa3lSkmCytfnftFjgB8C/PzaiyAD/NTw9vbq1qC0wWBAeUXV34sY71/KK6tgMj2+E+UDrq4ueGbRPMSPsG2BWJ2uHnv2H8X1W3c7XD9s8EAsWTALKpWvTfMgx3c555qwPIrFjURERERERERERGQF886NYaHBMmXSPVVV1fgsdRuqa2qEuIuLC1YsXYT4uBEyZdZ7lEolFs2Zik+3P5zwta6+CV/uz8Q3ls+SL7FuqKjWYcehs0Is0E+FKaOHy5QRORM3N1fEDIhCzID7Jyc2NDbhjrYEt+4V4nZBMXT1DU+4hYeMJiOqautQVVvX4Xpfby+h4PHRIkhvL8+nHhdtaDQvZKxEQ1OzeKXO53cFAPQLD8aapfOg8vV+qn12h9FoxNnLuTicccGiYyQA+Pp4Y8ncRAwfPMBmOZBzuJJ3S1geFB0BtcpHpmyIiIiIyFG8++67QjHfmjVrunU7cXFx0Gg07Z37PvjgA4cpbkxLS0NGRgbeeecdpKamIicn58kb9aD4+Hhs3boVDQ0N8PV17PNgt2zZgtjYWLnT6BYWNxIROSHzgTx3d3f4qR2nvfDps+eRfvCoRaeyyIgwvLB6FXx9+8bBPw93d7ywYiH+8Net7bGW1lZ8/uVOfH/tyw4zw2d9fQM2b98txHy8PfHs4lnyJEROzdfXByPjhmNk3HBIkoSKymrcvluAW3cLcLegCHqD5YDU06hvaER9QyMKCi1n6vTwcLcoeHzwfz+1CkqlEkajERWV1Y8UMpahrKIKRqOxg709xf308caalYsRFRnere2fhtFoROa5SziWca7DgTxvL08snDcD8SOGOsz7EcmnsakJN+9ohdjo+GEyZUNERERERERERETOwHzC1/BQx5nstai4BJ9t/hJNzWKhj6eHJ55fvQL9o7s+Q7+jmjIuARevXkPujTvtsTNZ1zE2dhDih0TLmNnTM0kSPtt9DG1m4ykvLZ8NDxt1waO+zdfHGwnDY5AwPAaSJKGyqhq37hXhdkEx7haWok2v7/ZtNzQ1o6GpGQXFZRbr3N3cLLo9Bvip4KfyQa2uQShk1D3lJLQdUSgUGDUiBovnJMLNzXanN94rLMGewxkor6zpcP3ouCFImjkZXp6O10mWet+VvNvC8qjhMTJlQkRERESOZMuWLcJyYmJit28rKSkJmzZtAnC/+2FaWhqSk5Otyq83vPnmm4iLi0NKSgpSU1N7ff/JycnYsmUL9u3bh+eee67X999TMjMzodPprPobkhOLG4mInFBZRZWwHB4a7DCFJ+cuXELagSMW8SGDB+K5Fcvg7u4uQ1bySRg+GJPHJuD0xavtsdz8m8g4exFTJ42TMbOnI0kSNn+1Bw2NTUJ89eI5UPeRIlWSj0KhQGhIEEJDgjB5whgYjUZoi0pw+64WFVXVqK6pRU2tzqrBPQBobW1DaXkFSssrLNYplUqoVb5oaGyCoZuFlfdvR4HQ4CBEhoeiX0QY4kcMhYeH7d4P79zTYu/B46iorO5wffyIIVg4dwZ8fGw3Syo5l6t5N4VJC5RKJeKGckCPiIiIiIiIiIiIus98wtfwMMcobqyvb+iwsNFPrcZLa55FSIhjdaC0lkKhwAsrkvHLjR+hpbWtPf75nuP4t+88C19vTxmzezrHzmbjxr0SITZ9fByGDYoCpCe0piOykkKhQEhQAEKCAjB5bDyMRiMKSypQUV2L6lodaurqUVNXj+oeGBdt0+tRVlmNsk7GELvLy9MDkWFBiAwLRmRYMPqFhdi0411DYxMOnLyAK7k3Olzvp/bF0nnTMPjvnTKJnsaVvJvC8qhYjoUSERFRL1EoAIVS7izk5yDnyT8qIyND6NoYFxdn1e0lJCQIy6mpqXZf3PjWW29Bp9Nh48aNsuWQmJgItVqN1NRUhy5u/PDDD6FQKJCSkiJ3Kt3C4kYiIidkXmDjKLOUlpdXIP3gUYv4uDEjsTh5PpTKvvnl+9kl85B38w7qdA3tsS1f7UFIcCCGxgyUMbMn23fwGC5lie3BR40YjHEJQ2XKiPoyFxcXDIiOwoDoh4NQkiShsbEJNXU61NTWobqm7v6/tff/NS/M7SqTyYTaOt2Tr/gIhUKBkOBARIaHtl/CQoLg5mb7mX3r6xuw/+gpXM293uF6Xx9vLEmaheFDOBBDXXM5+5qwPCxmALy87P+EFCIiIiIiIiIiIrJflmOioTJl8vQkScKOPWkWhY0R4WF4YfUqqFS+MmUmr0B/NVYtnoPPtqe1x+rqm/B/n+/Dj15eDC8bTvhorUt5t7HtwGkhFuinwjPzp8iUEfV1Li4u6B8Vjv5R4UJckiQ0NbdYFDw++LehqbmTW+xZHu7u7UWMkaH3Cxr91b69MmG3yWTChav5OHTqPFpaOy70nDBqBOZNm2DTiWbJ+UiSZNm5cQTH1ImIiIjo8TZv3iwsx8fHW3V75sWRD4on1Wq1VbdrKzk5OUhNTcXatWuh0WhkzSU5ORlbt27F1q1bHbLAMT09HRkZGYiOjkZUlGNO1MPiRiIiJ1RaXiksO8IspUajEdt37v17Z7OHB63nzpqOaYmTHKbzpC14e3nixRUL8fu/bm2P6Q0G/PHPn2PdP3wL/TX9ZMyuc0dOnsbu9MNCzMfbE2uWzenTzyfZF4VCAV9fH/j6+kDTL8JifVtbG2pq6lBT97DwsaZWh+raOtTW6WAymazOITgo4O9FjGGIDA9FeGjw37vU9t5MviaTCWcvZuHIyTNofWRW5AcUCgXGjYrD3BlTWJBG3XLFrGB2dPwwmTIhIiIiIiIiIiIiZ+GInRvPXbiEm7fuPDocikED+iPl2Wfg4eEhX2J2IHHcSFzKzkfu9TvtsYKSCvwpNR3fXZMMD3fbTwLZVTk3tfh4+2FIZt0ZX1o+G54sjCI7o1Ao4OPtBR9vL2giwyzWt7XpUatrEAoeHxRB1uoaYDQZu7xPdzc3RIQ+7MgYGRaMQH/1w/MFerGzaVFpBfYczkBx2YPzacRzFsJCArFk7tQOHxuiJyksqUBNXb0QY3EjERERET1JZmamsGxtgV9H2+/bt89uO/m9+eab0Gg0eOONN+ROBWvWrMGWLVuwYcMGLFy4EL6+jjMBWX19Pd58802H7toIsLiRiMgpWQzkhQbLlMnTO3L8FErLyoXY5InjMH3qZJkysi/xwwdj+qQxOHHmUnuspbUVv/3TX/G9V19EzMD+MmZn6eDRU9i+O12IKRTAC8vmQe3rI1NWRF3n7u6OsNBghHXwPmoymVCnqxcKHh/+v7bDIsHAAH+hI2N4aAg8PeU9WUFbVII9+49aFMY/EBkeisULZqFfBAfyqPuyzIobR8aygy8RERERERERERF1n8lkQlFJqRALD7Xv4sbKyirsP3RUiKl8ffHsiqV9vrARuF949dLKhdjwx09RXatrj9+4V4LffbYX330+Gd4yj6k86lLebXy8/TCMZhNhJk8fh2GDHHOGfOrb3N3dEBocgNDgAIt1JpMJuvpGVNd11PWxHq1tbXBzdUV4SBAiwx4WMwYF+EGpVMpwbx5qbmnFwZPncDH7ukUhMnC/k+TsxLGYODpW9lzJcV3Ouyks+6l80L9fWK8W8BIRERGRY9FqtdDpdEKsJ7oXqtVq4XZzcnKsvk1bSE1NRU5ODj7++GO5UwEAxMbGIjY2Fnl5eVi3bh0++ugjuVN6auvWrYNOp2NxIxER2ReDwYD9R04IsYiwUJmyeTr3CrQ4lXlWiIWGBGPe7BkyZWSfVi9dgPqGJlzOyW+PNbe04Lcf/A1rX16NhFj5u2CZTCbsSj+M9EPHLdatXjIHo+OGyJAVkW0olUoE+PshwN/PYp0kSWhubkF1bR3qdPXw8vRARFioXXU9bGxswsFjGbh0Na/D9Z6eHpg7fTLGjY7nQB5ZxWQyWRQ3jrKDzywiIiIiIiIiIiJyXDv3HkBVdY0Qi9b0kymbJzMajdi+cw8MBrHz2fIlyfD29pYpK/vjr1bhh99Owa//9BnqG5va47cLy/D+X3fh1WfnISzIX74EAZgkCcfOZmPbgdMWhVLTxsdhyZyJMmVGZDtKpRL+fir4+6ks1kmShLY2PdzcXO1qTFGSJFzOuYEDJ8+hqbmlw+skDI/BghkToeIEzWSlK3m3heVRI2LudyhlcSMRERERdSIjI8Miplarrb5dPz8/obgxOzvb6tvsaTqdDm+99RaSkpKQmJgodzrtfvGLX2DVqlXIyMjA22+/jbffflvulJ7o1VdfRUZGBhQKBf7jP/7DoTpOmmNxIxGRk9m8fQ/u3CsUYnNmTJEpmydrbW3FV7v2CQM/Li4uWLl8MVxd+TH1KBcXJV5Zswx/+OtWXLt5tz2u1+vxhz9/hmmTx2PlkiTZusCVlVfiky1f4fZdrcW6ZfOmYsbEUTJkRSQPhUIBb28veHt7ISoyXO50BJIk4cLlbBw6kYnm5tYOrzM6YQTmz0yEjw9PqCDr3bqrRWNTsxAbHc/iRiIiIiIiIiIiIuoeSZLwn+/9TojFDh+K4UMHy5TRkx07kYHikjIhNnHcWAyOGSRTRvYrLDgQP3hlNX770WY0PlKQVFRejf/+YBuWzZ6AWRPlmZixskaHT3cdw417JRbrJowcipRFM+4XsxD1IQqFAh4e7nKnISgtr8Kew5nQmr3vPhAS6I9Fc6ZiYHRkL2dGzior75awPHJEjEyZEBEREZGj6KijYk90btRoNNBqH57HbY+dG3/+858DAH75y1/KnIkoLi4Oq1evxpYtW5Camgq1Wo1//Md/lDutTr366qs4deoUFAoFYmNjsXr1arlTsgqrRoiInIjJZMKv3v+jEJs0bjSmT5kgU0ZPtm//YdTW1gmxOTOnIdzOu03Kxc3VFf/wjWfx0Rdf42reTWHdydPnkXvtBl5a/QyGD+29A6UmkwmHj2diV9oh6A0Gi/UrkqZj3rTxvZYPEXWuuKQMew4cQ1EnA3mhIYFYPH8W+tvx7NbkeK6YdW0MDQ5EeGgwIJlkyoiIiIiIiIiIiIgc2cGjJ3H+UpYQ+7ef/sBui8q0hUU4kXFGiAUHBWH+3JkyZWT/NJFh+Ml3XsD//nkL6uob2uN6gxHbDpzGpWt38NLSmb3WxdEkSTh+Pgc7Dp1Fm95yPHTa+DikLJoBpdI+/waJ+oqWllYcybyIs1fyLDqrAvfPt5g5eTSmjI2Hi6ubDBmSs7qcKxY3jmZxIxEREfUmheL+pa9zsMfg0QLEB/z8/Ky+XZVK1eG+eqJwsidkZGQgPT0d77zzTo90quxpb7zxBtLS0lBfX48PP/wQWq0Wv/jFL+yqI2JeXh5+/vOfIzc3tz1mb4Wi3cHiRiIim7Pmy1LXtt2x9xBy88WCt3/7x+9DoezG271VX/Kebtu8a9dxOStHuH7/6ChMmWRFIVwHB6i7xGS0Yt9WbNuF59rd1QXfeeEZ/G3bXpy7nCusq66tw28/+CumTR6PJUlzoFbZ9suUtqgEm7fvxp17ll+yFQoFnl82F1PHJ9g0ByJ6submFhw+cRrnL2d3OJDn7u6G2dMmYeLYkXBxcZEhwx5i7WeANeQ8OOJixWy4Vn12ATDqn3iVyzn5wvKouGF/f7ysfMyseboVvT+jt13su09yrAOXZNULu8ey6HXWfH452MH5dnJ+ZlvNQR9zIiIiIqI+x9rv7o78u0UmvTC2+MB/bhC7Ng4a0B8pq54BFN04vm3j39atra3YvmOPcGxeqVRi1fIlcHOzprDGir9Ra8ZDgac6LtwTIkOD8NPvPI/f/fVLlFfWCOtua8vw3x9sw/zE0Zg+bgTUvt42yUGSJFy/W4y9xy/iZoFlt0YASJ4+DkvmTHpMca217ydW/I3K+lYoY96w5ji8NRMzWjvuYc1xQmvH92T63Ouht2BJkpCdfxvpx86gobGpwxsfMXgAkmdNhp+6J86hcNBjhNZOPGrNGJe142PuVjxvkmVR+lMztD7xKg2NTbhVUCzERsUPB1w8ADx5+05Z9XRZO8msnGOpMr2+ZB1v4fgxkV3qi2O4RNSrOiputOW+7KW48a233kJcXBxSUlLkTqVDKpUK77//Pr797W9DoVAgLS0NGRkZ2LhxIyZPnix3enjvvfewadMmAPd/CysUCvz0pz/FiBEjZM7MeixuJCJyEpIk4b9+/XshlhA7HEuS58qU0ePV1zdg1779QszDwx0rli6CUsmDNk/i4uKCbz67BIOi++HrtKNobRMHEU+ePo/Mc5cwJiEWMxInIGZg/x6brVZvMOBSVg6OZ5zF7bsdf7kODvTDyysWYPCAqB7ZJxF1jyRJuJqbj/TDJ9HY1NzhdeJHDMGC2dNsXgxNfVdWR8WNRERERERERERERN1w6vQ5HDt1Woj980++D1dX+zz9Jf3gEdTU1gmx2TOmIiIiTKaMHEtwoD/+9fvfwI79J3A086KwTm8wYu/xC0g/eQljRgzE9PGxiNGE98iYaFNLK85k3cCJ87koq6rt8DqBfiq8uHw2hg+yj5MDifqq+oZGbN93DHe0xR2uD/RXY9GcRJ67QDZz9dptYRIDFxcXxA0ZKGNGREREROQIerO4UafT9dq+Hufdd9+FVqvF9u3b5U7lsRITE/H+++/jxz/+MRQKBerq6vDKK68gJSUFa9euRVRU7/++3L9/PzZs2ACtVtv++0OhUGD16tVYu3Ztr+djC/Z5dJeIiLrswNGTuHAlW4j960++a5eFgpIkYceeNDSZFdosSpoHf3/rW2r3FUqlAjMnj0Xc0EH4ZNte3LgjftE1Go04f/kqzl++isjwUMxInIi44UMQGODf5UE9o9GIkrIKXLh8FafOXERDY2On1501eTSWzZ8GD3drZpslImuZTCbsO3gc5y5d7XB9UKA/Fs+fhUEDOOhOttVh50YiIiIiIiIiIiKibvivX/+fsBwZEY5vvvCcTNk8Xv71m7h4WTxGr4mKxNQpE2XKyDF5uLtj9ZK5GB07GJ9uT0dljVgsajSZcD7nFs7n3EJkSAAmjhyC/pGh0IQHw8vT/an2YZIkVFTXQVtSifw7RTifcwtt+s47fU0bH4cV8xPh6fF0t09EtlFaXoXPd+yHrt7y/AVXVxdMmzAK0yaMtNsCeHIOl3NvCMvDY6Lh6ekhUzZERERE1Nf5+/tbxHqzkLIzWq0WmzZtwtq1axEXFyd3Ok+UnJyM//iP/8C///u/Q6FQQJIkpKamIjU1FcnJyfjOd75j826JhYWFSE1NxZYtW6DT6YSiRkmSkJSUhP/4j/+waQ69ib/ciYichHnXxsGD+mP1iiUyZfN45y9exs1bd4RY7PChGBkfK1NGji040B/rvp2CE2cv4+v0YxZdHAGguLQcm7fvBgD4eHtBExWJ6KhIRPeLRGhIENzcXOHm6gpJkqA3GNDS0obi0jIUFBajoLAYRcWl0Bs6H8C7n4cfXlqxAEM44yGR7Fpb2/DlzjTcuH3PYp2bmytmTJmAKRNGcyCPbK66pg6FxWVCbFTsUJmyISIiIiIiIiIiIkd2OSsHe/cfFmLrf/gP8PCwvwKChoZG7NybLsTc3d2wctliu5yc1hEMGRiFf/vhN7DzwEkczbzU4XWKK2rw9aGz7cshgWpEhwcj6u+Fjm6urlAqFTAYjGgzGFBd24CCkgpoSyvR0mo5xmqO3RqJ7MeNO1ps3XMYbR2cHzFkoAYLZ09BoL9ahsyor7mSd1NYHjVisEyZEBEREZGj6KyTolptm98wtbW1Nrndrli3bh3UajXeeOMNuVN5aikpKdDpdHjvvffaCwoBIC0tDWlpadBoNEhKSkJiYiISEhLg6+tr1f4aGhpw9epV5ObmYu/evcjNzQUAoajxwXJycjLef/99q/Znb3g2MxGREzh5+jyOZ5wTYv/0o+/AxcVFpow6V1lZhf2HjgoxlcoXSxbO73I3QXrofhfHMYgbOhD7jmTifNa1TosRG5uace36LVy7fqtH9u2n8sG0CSMxd+o4dmsksgO6+gZ8/uUulJZXWqwbNnggFs6bAX8/DuRR77hi1rXRw8MdwwYPkCcZIiIiIiIiIiIicmj/9evfCcvBQYH4zisvyZRN5yRJws69aWhsbBLiCxfMRUCAvzxJOQkPdzc8t3g2Jo+Jw9HMS7hw9Rr0BmOn16+o1qGiWocLubet2m9YcACmj4/DlDEj2K2RyA6cu5KLfUdOw2QyCXE/tS+SZ07G8MH9ef4J9ZqsXLG4cWQsixuJiIiIyL7U19fLuv/U1FTk5ORg48aNsubRHa+99hri4uLw4x//GDqdTihyLCgowKZNm7Bp0yYAQGxsLOLj4xEdHQ21Wg0/Pz+oVCpoNBr4+/ujtrYW9fX17f/W1dVBp9O1FzQ+2mHzwT4AsagRANavX4+1a9f21kPQa1jcSETkBP77N38QlqMiw/GNlBUyZdM5o9GI7Tv3QK8Xi+6WL06Gt7e3TFk5l+BAf7y8aiFWLpyF0xezcfzsZVRU1dpkX0MHajB90iiMGj7ILgtpifqiktJyfL5tN+obGoW4q6sLliXPwci44TJlRn3VZbPixrhhMXBzYyE8ERERERERERERdc216zfx5Y69QuzH330VPj72N8Z44dIVXL8hFtMNHzoYo0fGy5SR89FEhuLlVUlYsXAGTl/MwYmzV1BZXdej+1AqFRg1PAYzJiZgSP8IFkoR2QGTyYQDJ84i80K2xbrofuFYs2wevL08ZciM+iqj0Yisa+LE4qNjh8iUDREREfVZCuX9S1/XyWNQUVFhk935+Ph0u1NfXV3PHsOwZzqdDhs2bEBiYiKSk5PlTqdbEhMTcfDgQaxbtw6ZmZnCMaJHixBzcnLauy121aO384D5fjQaDTZu3IjY2Nhu7cPesbiRiMjBXbySjX0Hjwmx9T94De7u9jdj5PGTmSguKRNiE8ePweCYgTJl5Lx8vL0wd9oEzE4cj2u37uLUuSxcv6NFY1OzVbcbFOCH+GEDMX3iKESEBt0PdvCFioh63/Wbd/DlrnS0temFuLeXJ9asXIzoqEiZMqO+LMusuHFk7DCZMiEiIiIiIiIiIiJH9v/e/4Nwko9arcL3135Dxow6VlVVjfSDR4SYr68Pli5awOI4G/D19sK8aeMxJ3Ecrt26h8s5N1BQXIbiskoYjaYn34AZla83oiNCEBMdiUmjh8Nf/feTFKWu3xYR9Sy93oDt+44i7+Zdi3UJw2OwfMF0uLryVEjqXbfuFaGpuUWIjRrBzo1ERERE9mTVqlU2ud0f/OAH+OEPf2iT27aGSqWSOwXBz3/+c+h0Oofs2vgotVqNjz/+GKmpqXjvvffauziaFyB2VKT4NDo7bvjg9lavXo033njD7p7fnsRf9EREDu5X7/9RWA4JDsTal1fLlE3ntIVFOH7qtBALDgrE/DkzZcqob1AqFYgdMhCxQwZCkiRU1+pQUFSKguIyFBSVQVtSjsamJov6RKVSiQA/FaIjQxEdGQZNvzBoIkPh6+0lzx0hosc6ezEL+w4et/hhFBTojxefXYrAAH95EqM+70rudWF5VNxQmTIhIiIiIiIiIiIiR3WvoBCfbvlKiH1/7Tfg7+8nU0YdM5lM+GrXXuj1BiG+bFESfHx8ZMqqb7g/JjoAsUMGAAD0BgNKyqrax0SraurQptfDYDDCaDTCzc0Vbq6u8PRwR1R4MDSRoegfGQo/lQ+LUInsUENjE77YcQBFpZYdV2ZOHoNZU8bytUuyuJJ3U1gOCwlEWEigTNkQERERkaPQ6XS9ur/a2tpe3d8DGRkZSE9Px/r166FWq2XJoaelpKQgJSVFKHIEYFHoaK2+VNT4AIsbiYgcWF7+TWzblS7EfvwPr8DbzgrQ2tra8NXOvULRjVKpxMrli+Hm5iZjZn2LQqFAUIAfggL8MCb+Yecsk8kAo9EEvcEAhUIBN1dXuLiwRT2RIzCZTNh/5CROn79isa6/JhIpzyyyu88E6jva2vTIyRcH9EbHDZcpGyIiIiIiIiIiInJU7/7vn2AwPCwY9PLyxI+/+6qMGXXs+MlMFBaVCLHxY0dh6JAYmTLqu9xcXRHdLwzR/cKACX8PGvWy5kRE3VNeWY3Pvt6POl2DEFcqlVg2fxpGc2JNktHlnBvC8uhYdm0kIiIioiezZaFffX29Rczf399m+3uct956CxqNBq+99pos+7elR4scU1NTkZub276uO0WOj9ZYqNVqrF69Gq+//nqfKGp8gMWNREQO7H9++4HwYeanVuF7r74kY0YdSztwGNU1tUJs1vREREaEy5MQCRQKBVxdXeDq6iJ3KkTUBW1tbdi2az/yb96xWDcybhiWJc+Bqyu/7pN8rt28YzFD+chYDjATERERERERERHR0ystK8emv20WYq9943mEhgTLlFHHCouKcfzUaSEWFBiABXNnyZMQEZETuH2vCKm7DqG1rU2Ie3q4I2XpPAyMjpQpM6L7svJuCcsjR7C4kYiIiIgIAN59911otVps375d7lRs6kGRY319PTIyMrB3715kZmZ2uTtnYmIiEhMTkZSUBI1GY6Ns7RvPdiYiclB3Cwrx6dYdQuz7a1+Gn9q+KvTzr9/ExctXhZgmKhLTEifJlBERkeOrr2/AF9v3oLi03GLdrKkTMXPqxB5tcd9VtboGaIvLUVZZg9Y2PQwGAwxGE1xdXeDu6goPDzdEhAZBExkKX3aWdFrHMy8Iy9H9IhDgb7tZp4iIiIiIiIiIiMj5/Ob3m9Da2tq+7ObmhvU/fF3GjCy1tbXhq517YTKZ2mNKpRIrly+Gu7u7jJkRETmui1fzsfvQKeG9FQAC/FR44ZkFCAkK6PWcJElCta4R2tJq3Cupgra0ClV1jWjTG2AwGGGUJLi5uMDNzQXenu7oFxqA6PAgRIcHoV9oANzdeKqmM6mp0+HYmUtCbBSLG4mIiEgOCsX9S1/Hx8BuaLVabNq0CSkpKYiLi5M7nV6hUqmQlJSEpKQkAPc7aGq1WtTV1UGr1UKn06G2thYAEB0dDT8/P2g0GqhUqj5bzGiOv5iJiBzU//z2AxiNxvZlb28v/PgfviVfQh1oaGjEzr3pQszd3Q0rli6CUqmUKSsiIsdWVl6Jz7ftRp2uXoi7uCixLHkuRsUP79V89AYD8m9rca+wFAXF5dAWl6OuvvGptw/0VyE6MgzRkaEYqInA4AH9+BnhBIxGI3676TMhNnFMvEzZEBERERERERERkSOqrqnF7z/6RIh9Y80qaKLsq1PX/kNHUVVdI8RmTJ2MfpERMmVEROS4JEnCoZPncfLcFYt1URGheH75fPj04uSpLa16nMu5gyvXtSgorUJjc+tjr9/893+rAGhLq3E6635nP6VCgYgQf8REhSJx9BBowgJtmzjZ3O//9hUaGpvbl5VKJWZMGi1fQkRERETUoW3btiEkJKTHb9fHx6fb2/r5+fVgJk+mUvVu46S33noLarUa69ev79X92hOVSoXY2FgAwJQpU2TOxjGwuJGIyAGVlJbj48+/FGKvfSMFwUH2c/BTkiTs3JuGxsYmIZ48fw4CA3t/Bj0iImdw8/Y9bN2ZhtbWNiHu5eWBlGcWYUB0VK/lUlVTh5PnriLjYo4waNNV1bX1qK6tx+XcmwDuFztOG5+AxHHxUPl691S61Mu27zmEm3cKhNh3vvGcTNkQERERERERERF1laPO9C51e0uF1bPbW7G9ouMJ7/73g7+goeHhZHpKpRL/8pPvi9eXNW8Frt+4hfOXrgg3E9UvEjOmJz4+N6n7z9X97U1Pvk6nrHzMOnm+nopVeVvJmr8Va54uOTtHWPlnZt2fiox/J9Y85lb9nVj7gFtxv63etzVPtrX7fkivN+Dr9OPIuX7bYl3c0EF4JmkG3ITuh7Z7fRVX1OLExXyczb6Nlja91bdnkiQUldegqLwGxy/mY2C/EMwYOwxjhveHm6vL09+Q1c+1FZQ9/1nfK6x5zDrZtrmlFb/9eKsQW714NqLCQ8y2seJ+K6zJm5MJ9xl9tUOVnO8p5EBk/H5kzWdPX31dE9lQSEgIwsLC5E7jqeh0OqjV6h6/XX9//x6/zc6kpaUhIyMDGzdutMl9IefF4kYi6iL7OKDZu6wd3On+9p0NIv7mD38WClvc3Nzwxg9es7y+0pq3eevu98XLWbh+QzzgPHzoYIwZlfAUW/f8gcWnZtWPQ2sGaBz19SEjWZ9rot534XI29hw4CpNJ/NsP8FfjxWeX9kqBu8kkIe/mXRw/m4Wc63ds8tZVXVuPnQczsOfIaYyJG4IZE0ZiUHTEU55YI+d7qYwn0FjDBieSSJKE//ndn4XY2JEjMHf6pJ7eUw/fHtmOtX/ffK7J3sl6lpgVHDVvIiIiIiIi6gvq6xuw8Q8fCbGUlUsxOGagTBlZamxsxM49aULMzc0NK5YuglLJE66JiLqisakZm3cehLa4zGLdtAmjMHfa+B4oxH88SZKQdUOLI+fycKPAMo+edKeoAneKKrDt4DlMGTUYcybGQu3Tex0pyTp/2boXFVW1QuyN11+QJxkiIiIicji2LPqrra3t1f09SqfT4c0330RiYiKSk5N7ZZ/kPFjcSETkYKpravGHjz8XYt96fiX6RYbLlJGl6uoapB04LMR8fLyxdNECmx9sJiJyNpIk4eCxDJw6c9FinaZfONasWAwfH9t3OLxXWIpPvz6I4rLKp7q+h4c7NJER8PX1gZurK1xcXGAwGqDXG1Cnq0dRcSn0BkOn2xuNJpzPysf5rHwMGdAPLyyfh9Ag/x66N2RLh0+ewYWsXCH2T9//Nr8DEBERERERERER0VP708efoqa2Toj96z/+QKZsLEmShF1796OhsVGIJ8+fg6BemIyQiMiZVFbX4rOv0lFTVy/ElUolFs9JxLiRw22eQ219E77Yl4nsW0VPvK63lxeiNZGIjopEVEQ4vDw94OrmCqVC2T4eWlldg4LCYmgLi1FaXgmpk1ljG5pbceB0Dk5dvoHn5k/EhLiBHFOzcwaDAe99uFmILZg+AWPih8qUERERERE5IrVaDZ1O1yv78vPz65X9bNiwATqdDu+8806v7I9E3/72t1FUVISPPvoIUVFRcqfTZSxuJCJyML/901/R2NjUvqxUKvHP616XMSORyWTC9p17oNeLBSvLFyfDx8dHpqyIiByTXq/HV3sOIDf/lsW6+BFD8MyieXB1te1Xer3BgL2HT+PAyQudDroBwMD+URjYX4PoqEj0j+qHkODAx85MbTQaUVJWAW1hMQoKi3Hjzj0Ul3Q8A+qNu0X4r99/huXzEjFz0mgolRzQs2f/87uPheWYARqsWjJPpmyIiIiIiIiIiIjI0bS0tOC9330gxJYtWoCEuBEyZWTpclY2rl2/IcSGDo7B2NEjZcqIiMgx3dWWYPPOA2hpbRPiHu7uWL1kDmIG2PaETEmScObqbXx56ByaW9o6vI5CoUBC7DBMGDMS/TX9EBTo36UCxNbWNhQWlyDv+i2cOnMBdbp6i+s0tbThr7tO4tK1e1iTPAl+vraf3Ja6Z9u+Y7hdUCzE/um7L8qUDRERERE5Ko1Gg5ycHCFWV1dndZfF+nrL3xsajcaq23waOTk5SE1Nxfr163tlf2SptrYWBQUFcqfRbSxuJCJyIPX1DfjtB38VYs+vWoqYgf1lysjSqcyzKCwqEWLjx47C0CExMmVEROSYGhoasfmrvSgsLrVYN33KeMyZPtnms3beKyzF37bvR2lFdYfrPT09MHn8aEyfPAER4aFdum0XFxdERYYjKjIcUyaOhSRJuKctwvGMszh/ORsGs66Oer0BX+47jku5N/HSM/PZxdFOXbiSiwPHMoXYT7/7Tbi4uMiUERERERERERERETmajz/dgtKyciH2bz+1n66NTU1NSDtwWIj5eHtj2eIkdtsiIuqCyznXsevgKRiNRiHup/bFi88sQGiwbTvhPqlbo6+PD6ZOHodpk8YjKNC/2/vx8HBHzMD+iBnYHwvnzURWzjUczziL/Jt3LK6bdUOLm9oydnG0U5Ik4X/++JkQmzBqBGZPGStTRkREREQAFMr7l77OwR6DqKgoi+LGnujkWFdXZxGLi4uz+nafZN26ddBoNHjttdesuh3zx6CjYk3q2IPHyt/fX95EuonFjUREDuSPH3+OmlrxS8e/2FHXRoPBgIwz54VYYIA/FsydJU9CREQOqqKyGp99uQO1deIPM6VSgaVJczBmZKxN9y9JEvYdPYN9R8/AZLLs1hgRFoLZ06dg/JgEeHp49Mg+FQoFBkRHYUB0FFYuTUbmuYs4evKMxeferXvF+K/ff4bnFs3E1HHxPbJv6jnv/p/YtTE0OBDfXL1MpmyIiIiIiIiIiIjI0ej1evy/jb8XYnNnTsOk8fZTNHA5KwetrW3AI/UmyxYnw9fXV76kiIgciCRJOJp5EcdOX7JYFxkWjOeXz4fK18emOdwpqsAfthxCYwfdGtUqXzyzeAHGjY6Hm2vPnl7p4uKCMSPjMGZkHErKKrBn/xFcvJItXOdBF8cbBaV4PnkylErHOknbmR08eR6XcsTOzf/0Dy+wCJWIiIiIuiwhIQHp6elCrKPCxK4yLw7sjS6KWq0WWq0WADBs2LAeve2MjIxObzMlJQXvvPNOj+7PkWm1Wof+bcLiRiIiB9Hc3IL3fv+REHtm8XzEx/bslwBr5OXfQHNLCx4dyVu5fDHc3d3lS4qIyMHcvluALTv2oaWlVYh7enpg9fKFGDTAtj82TSYTNu86jFPnsy3Wubi4YOG8mUiaM92mnfh8fbwxf9Y0TJ8yATv2HMCxjLPCer3egM93HEJ9QxOSZkxw6B9kzuTWXS2+3H1AiP3otRfh5eUpU0ZERERERERERETkaL74cgfuFRQKsZ+t/6FM2ViSJAkXL2cJsbgRwzFs6GCZMiIiciwGgwE79p/A1Wu3LNYNH9wfK5Nnwd3dzaY5XLtTjD9tO4o2vcFi3YQxI/HcM4vg6+Nt0xyA+xPKrn15NS6OjMPm7bvR0NgorM+4chPNrXp8c+k0uLnabmyWnp5518YhA6PwzILpMmVDRERERI6so26KDwoErWF+G7Gxtm2kQfYhNzdX7hSsxuJGIiIH8fHnX6KsvFKI/dtPvidTNh27cEkcyBs4IBpR/SJlyoaIyPFcysrFrvTDMJlMQtzfT4UXn12GkOBAm+7faDThr1+m4UL2dYt1mn4ReDllBaIiw22aw6M8PTyQsnIJRo+MxadbdqCqukZYv+tQJppb2vDMgqkscLQDG37/F+Fv19fHG9/9ZoqMGREREREREREREZEjMZlM+O9f/06ITZ4wFrOmJ8qUkSVtYREqq6qF2IRxo+VJhojIwTQ1tyB110HcKyy1WDdlXDzmT59o8y6FObeK8MG2IzAYxfFYtcoXa1Ytxej4ETbdf0fGjorDkJgBSP1qj0UXx0vX7qFNb8B3Vs2Cqw0nn6UnO591DYdOXRBi67/zvE0nBSYiIiIi55WYaHm8q6CgwKrbNO/aCNzvEGlrGo0G+fn5Vt+OVqvFvHnzhFhSUhJ++9vfWn3bzqywsBDr1q1rX/b19ZUxm+5jcSMRkQPQ6/X4n99+IMTmz5qGCWNHypSRpaqqatwtEGd7GDfafvIjIrJnkiThyInTOJ55zmJdv4gwPL9yMXx9fWyag8kk4dOvDlgUNiqVSiyaP8vm3RofZ9jgQfjZT7+Hr/ccwHGzLo4HT12Am5sLlsyZIktudF9ZRRX+krpDiH3n5WcR4K+WKSMiIiIiIiIiIiJyNF/t2odr128KsZ+t/5FdTW5nPtlrUGAg+kdrZMqGiMhxVNXU4fOv96Oqpk6IKxQKLJozBRNG2b6byE1tGT7cftSisDF22GB864Vne6VbY2dUvj5Y+/JqnI8fgb+lfgWD4WFXyZxbRfjrrpN4Zdl0mxd/UufMuzaGhwTi5RVJMmVDRERERM4gLi4OOTk57cvWdm7saPvk5GSrbpPsV2ZmJlJTU5Geni53Kj2CxY1ERA7g8y934p62SIj97Kf21bXxUpY4e5y3txeGDxsiUzZERI7DYDDg670HkZ1n2S1xxNAYrFwyH25ubjbNQZIkfLn3KM5eyRPiri4u+PbLq2WZodScp4cH1qxcgrDQYGz9eq+wbt/Rs/D29MCcxDEyZUf/73d/RmtrW/uym5srfvL6yzJmRERERERERERERI5EkiT854b/FWIj40dgcdJcmTKy1NLSgtxr4iz0Y0cn2FXxJRGRPSooKsUXOw6guaVViLu7u+G5xXMwZKDti8SLK2rxx62HoTcYhfjYUfH41vMr4epqH6dRjh+TAJXKB3/8+HNh7O1i3j34enkiJWmSjNn1XTv2n8C2fceE2Lpvr4anp4dMGRERERGRM0hJScFbb73Vvpybm2vV7WVni+fyazQaaDSclMuZ5OXlYe/evdiyZUt7p05Jkpzi+KR9/ConIqJOGY1G/Pf7fxRiiRPHYkbiRJkysmQ0GnE5K0eIjUqIs5uDv0RE9qqpqRlfbN8NbVGJxbqpk8Zi3szEXvnRcfpSLo6duSLE3Fxd8d1vv4jhQ2Nsvv+umD1tMrw8PPDJlq8hSVJ7fHv6CWgiQjBkYJSM2fU9ra1t+NHPf4UPP/lSiL+4agn6RYTJlBURERERERERERE5mvRDRy0mU/23f/yhXZ2YczUnD3r9w05aSqUSoxLiZMyIiMj+Xb12C1+nH4fRKBYVqlU+eGH5AoSHBtk8B6PRhL/uOoHmVr0QnzB2JL65ZqXddUMcNngQfvSdb+K3f/orWtseFjgev5iPEYMiMXIIT07uLUajEW+//zH+83//KsTVKh/8w4vLZcqKiIiIyIxCASjt5/iJbOzoGNLTMi9u1Gq10Ol0UKvV3bq9U6dOWdw+Ob7CwkKkp6cjNTW1vTvno+fO2tPxU2vY1y9zIiKy8NXu/ci/cVuI/eyn37OrD6LrN26hobFRiI0dlSBTNkREjqGqugabPt1qUdioVCqxZMFszJ81tVfe62t1DRazTCqVSnznm2vsrrDxgckTxmDNyiVCTJKAT78+iNY2fSdbUU/TFpVg5jPfsihsVCgUeON735InKSIiIiIiIiIiInJI5l0bh8QMxLPPLJYpG0uSJOHCpSwhNmzIYPj6+sqUERGRfZMkCcfPXMK2vUcsChvDQ4Kwds2yXilsBID0zKsoLKsRYgmxw/CNlBV2V9j4wMD+Grz+yvNwdXER4l/sO43G5tZOtqKeVF1ThyWv/JNFYSMArHvlOfip+R2AiIiIiKxnXoCYkZHR7dvKzMx87G0/DZ1Oh9TUVLz77rtW5ULWaWhowEcffYRVq1Zh/vz52LBhAwoKCiBJUnunxgcXZ2Gfv86JiAjA/YO9//WbPwix0QmxWDhvljwJdeLilavCsiYqEiEhwTJlQ0Rk/+5pi7Dp062orqkV4h4e7nhh1VKMH9M7BeKSJOHzHQfR3NImxL+5ZiXiRgztlRy6a/qUCViaPEeIVdbosOPAqU62oJ505OQZjJ+/GmcvZVus+89/+xFGDB0kQ1ZERERERERERETkiI6fOo2TmWeF2L/85PtwMSvokFNJaRlKy8qF2LgxI2XKhojIvhmNRuzYfxyHT12wWDd0UDS+nbIEapVPr+RSWFaNfafE4vSIsFC8+vJqu/qc6cjwITFYs2qpENM1NmPrgXMyZdR3XM65jgmLv4n042ct1r34zHz8/IfflCErIiIiInJGr732mrCcmprardtJS0uDTqdrX167dm2XO0BqtVrMnTsXb731FjZt2oRXXnkFP/rRj7qVD3XP1q1b8eqrr2LChAnYsGEDcnJyOi1ofBB/tIujI3OVOwFyFtZU/DrHi4l6gTWV5Q76Z7bv4AlcysoRYv/2j9+HQvmUb98KK2rYn/LxrqvT4ebtu0Js3JhR3d8vAEgmebYFYNX7mYx5y/rFxIlmfSAnZWdf3LNyrmHHvkMWM5SqVb548dllCAt9UBxu7Wvryff79KVc5Fy/K8QmjhuFCWMd44SMpDkzkJt/E7fuFLTHjp3JwugRgzB0YFTXb9CavxWFlZ8/T/vZ3hGrPn+6dp8lScKv//hX/MsvfmPxN+zt5YkPf/02nl+xqPv50BP01c9cGe83v+eQvbPqs8v23zUes3Mr920NGb8bdvR82dl3VSIiIiIicnSOeSzjv977nbCsiYrES2uefbqxTmt/3z7l9hevXBUeXj+1GoMGDrBix1b+HrTqZ7l8xwQkyfjkK3W+cfe3tZas5yrIePzHqr8za+bbl/G4lTVjLla/BVvzmFl7noQVzB7u1tY2bN55AHe0JTB/UCaOjkXyrMk91y3xCX/jRqMJn+zJgMn0MEmlUolvrFkBdze3nsnBxqZMGINLWTnIuXajPXYu5zbGDtdg5JBujIdaw4q3cLjI+Lo2dS3xT7btw+v/8iu0tIoTBLu6umDDz36AH35r1dN3R+mD57rJOrYm67ieg/aZseoxs/I+cxyWnoasY6EysfZ3j1WfPdY83t3flKiv02g0SElJaS9qzMjIgFarhUaj6dLtfPDBB+3/V6vVeOONN7qcy7p164QCSQBIT09HRkYGEhMTu3x7XWW+bwCor6+3+X7ltn//fuzduxfp6entsUfrAsx/fzxYFxsbi8TERERHR2PDhg0dPn6OhMWNRER27Ffvi10bhw0ZhJVLk2XKpmOXrlwVPkA9PDwQO9y+u30REcnlwuVs7Eo/bBGPCAvFC6uWQKXy7bVcanUN2LbvmBBTq3zx3PKFvZaDtZRKJV5evQL/+evfQ6/Xt8c/3XEY//bdNfD0cJcxO+fT0NiEtT9+E1t2plusixmgwfaPf4MEO+/4SURERERERERERPblxq07SD90VIj907rvwd3dfo7vtrW14Wp2nhAbMyqh54pziIicRFubHp/v2I97haVCXKFQIHnWZEwaE9er+Rw4nY3CsmohNn/2NPTX9OvVPKyhUCjw4nPL8Yt3f4fmlpb2+BdpZzBEEwovT/v5vHR0bW16/PSXv8X//eVLi3VhwYHY8vt3MH2ilZO9ExERERF1YP369e1FjcD9IsPt27c/9fZpaWnIyXnYTGnjxo3dyuPR23jUqVOneqW48cH9f1LMGWRmZiItLU3ouPk0BY0ajQZJSUlISUkRCmDVajV+/OMf2z5xG+KRViIiO9XS0opTZ84LsX/58Xfh4uIiU0aWTCYTLmVlC7GEuOF2NdhIRGQvLl/Nxe79RyziwwYPwisvrOzVwkYA2HP4NJpbxNkmX3h2GXy8vXs1D2uFhgRh+cK5QqyqRodjZ7Jkysg5Xb91F1MWvtBhYePieTNwLv0LFjYSERERERERERFRl+Xm3xCW1WoVXv3G8zJl07Gc3Hy0trW2LysUCowZlSBjRkRE9kevN+CLDgob3dxcsWbZvF4vbGzTG3DojHhibkRYKBbNn9WrefQEfz81nl0mToSua2xBZtYtmTJyPsWlFZiT8v0OCxunjI3HhT0fsbCRiIiI7JdCwcuDi4NSq9X4+OOPoVarAdwvMnz33XefatucnBy8+eab7csbN27sdiHig/2bi46O7tbtdUVGRgY2bNhgEddqtXjrrbecosgxLy8P7733HiZNmoRvf/vb2LJlC+rq6iBJEiRJgkKhaL8AaI+rVCqsXbsW27dvx4EDB7B+/XqLzp7JyfbVPKs72LmRiMhOKZUKmEwmITZ+9EiZsunYrdt3UVcntjAeN4YH84iIzGXlXMOOfYeEmVUAYPL40Vgwe1qvz+7c1NyCc1nXhNjEcaMwMm54r+bRU2ZNm4xLV3Nx605Be+zE+WzMnzaWM2f3gB37DuObP/w36OobhLhCocC/r/8ufv6T7/BxJiIiIiIiIiIiom7x9xNPmpIkCV5eXjJl07GLV8TJ9AYPGgg/v45P9iIi6osMBgM27zyAO9oSIe7t5YmXViQhMjyk13O6kHsHTWYTvb6csgJuro55uuTkCWNw4Uo2cvNvtsdOXLqBWROGQ+nAJ3Hbg5Nnr2D1d3+G0ooqi3XffXkFfvPmD+Hu7iZDZkRERETUl2g0Gmzfvh2vvPIKtFotNm3ahPr6erzzzjudbpORkYF169a1d/7buHGjVUVu69evx1tvvSXE1Go1Fi5c2O3b7Mhbb72Fffv2AUB77o+TmpqK1NTU9nwAwM/PDx9//LFFkZ+9KSwsRHp6OlJTU9sLNJ+mQ6NarUZycjLWrFmD2NjYp9qXWq1GfX19D2Xe+xzz1zoRUR/g7u6OoMAAVFXXtMdKysoRHztMxqxEFy+LA3kR4WGICA+TKRsiIvuUc+0GvtpzwKKwccaUCZgzY4osOZ2+lAu93tC+rFQqsWLxAlly6QlKpRKrlibjf377QXuspq4B2dfvYeTwgTJm5tiMRiPefvf3+M/f/Mlinb+fGp/+/ldYNHeaDJkRERERERERERGRs4gICxWW6+sb0NjYBB8fb5kyEpWXV0BbWCTExtrZhLRERHIyGAxI3XUIt+6J75Venh745rOLEBYSKEtexy/mC8sjhsZgQHQ/WXLpCQqFAgvmTBeKG8tr6pF/txQjBkbImJnjkiQJv/vLl/jpLzbCYDAK6zw93PGH//onfHNVkkzZEREREVFfpNFocPDgQbz77rvYtGkTUlNTsW/fPqxevRpTp06FRqOBTqdDdnY20tLSkJGR0b7dxo0bERcXZ9X+U1JSAAAffvgh6urqEB8fj3feeafTjo7dVVtb+1RFjR15sF13t+8NDQ0N2LdvHzZv3ozc3FwAjy9ofHR9UlIS1qxZgylTun5ucVRUFIqKip58RTvF4kYiIjsWERYqFDeWllXImI2ooaER+TduCbGxoxNkyoaIyD7lXb+JbbvSLQobp00eh9nTJ8uSk8kk4cRZsTh9zMhY+KlVsuTTUwZERyE6KhIFhcXtsePnrrK4sZuqa+rw4nf/CelHTlmsGxk7FNs+fh8xA6IBydTB1kRERERERERERERPx7y4EQBKy8oRM2hA7yfTgYuXrwrLvj4+GDokRqZsiIjsi9FoxNY9h3HjjlaIe3q44xurFspW2HivuBIFJWIXvplTJ8mSS08aMmgAIsJCUVJW3h47cfE6ixu7oam5Ba//y6/w2VfpFusGaCKw7U//jTHxwwCToYOtiYiIiIhs64033sDrr7+Offv2ITU1FZs2bcKmTZssrpeYmIiUlBSrujWaS0lJaS9ytJVf/vKX+OUvfynEnlRAaV7MWFdXZ3ddG/fv34/U1NT2otOn6dD46Pq8vDyr9r99+3artpcbixuJiOxYRHgosvMezib36AFKuV3OyobJ9LCgwc3NDQlxI2TMiIjIvuTfuI2tO9KE90oAmDJhDObOSOxw9pXecP2OFuVVtUJsxpSJsuTS02YmTsQnW75uX867WYDyqlqEBvnLlpMjupx9Date+THuFBRarHth5WJ88N7b8Pb2kiEzIiIiIiIiIiIicjYqlS98fLzR2NjUHispK7OL4kaDwYArV7OF2OiR8XBxcZEpIyIi+2E0GvHlnsPIv1UgxD3c3fDyqmREhAXLlBlw/OI1YTnQ3w/xI4bKlE3PUSgUmJE4Ealf7W6PZd0sQo2uEQFqHxkzcyy37xVh1ev/iiu5NyzWLZgxCZ/9738gKMBPhsyIiIiIiB5Sq9VCoaFWq4VWq4Wfnx/UarXdFfZ1RXc6QZpv09PdJLsrMzMTqampSE9/OHFKZ0WNj8YTExORnJyM1atXY/jw4b2TrJ1jcSMRkR2LCAsRlu2luFGSJItZSuNGDIOnp6dMGRER2Zcbt+5iy459FoWNE8eOxILZ02QrbARg0bUxIiwUgwf1lymbnjVudDy27UpHU3Nze+zk+RysTJoqY1aO5ZOtO/H6+v9AS0urEHd1dcWGt9fjh2tflPXvl4iIiIiIiIiIiJxPeGgobt25275cUmofY6J5166juaVFiI0dPVKmbIiI7IfJZMJXaceQd/OuEHd3d8XLqxaiX7hlV97eojcYcSH3rhCbNmU8lEqlPAn1sInjRuLrvfvR2toG4P75O2dz7iBpSrzMmTmGfUcy8eIP/x21unqLdf/6g2/inZ++xkkMiIiIiMguaTQahy5odCZ5eXnYvHkz0tLS2jtKPk2XxtjYWCxatAgpKSlQqVS9l7CDYHEjEZEdCw81K260k4G8ewVaVNfUCLGxoxNkyoaIyL7culOA1K/3wmg0CvHxoxOwcN5MWQvDTCYJ18xmT52RONFpitXc3d0xZcIYHDqe0R67dlsrY0aOo61Nj5/++7v4vz9/brEuLCQIWzb9GtMnj5MhMyIiIiIiIiIiInJ2EeHmxY1l8iXziIuXxckCB/aPRmBggEzZEBHZB5PJhK/TjyE7/5YQd3d3xUsrkhEVIV9hIwAUllVDb3g4TqtQKJA40XnGuLw8PTF+dAJOnbnQHrtTVCVjRo7BZDLhP//3L3j715uEk44BQOXrjb/+5i08kzRTpuyIiIiIrKRQAArnmMzDKk5yDiTZp8LCQqSmpmLLli1dKmjUaDRISkpCSkoKi1OfgMWNRER2LMJsNjt76dx44ZI4kBcSHARNVD+ZsiEish937mnxxfbdMBgMQnxMQiwWL5glexFhRXUtWv4+i+cDo+Kdq6X9yLjhQnFjSXk19HoD3Nz406czJWUVWL32H3Hq7CWLdVPGj8LWj36DSBln2CUiIiIiIiIiIiLnFhEeJizbw5hodXUN7twTJwtk10Yi6uskScLO/SeQlXdTiLu5ueKF5UmI7hcuU2YPaUvFQr+wkGCoVb4yZWMbQ2IGCMWN2jIWNz5OS0sr1nz/Tew8cMJi3YjBA7D9w19hWEx/GTIjIiIiIiJ71tDQgNTUVKSmpkKrvd9k42kKGtVqNZKTk7FmzRrExsb2XsIOjmf4EhHZsYgw8+LGCpkyeai5uRl5+TeE2NjRI2Uv2CEikts9bRE+32ZZ2DgqfjiWJs+xi/dJbbE427Va5Qs/tXO1t9f0i4BCoWj/oWgymVBUVoUBUWFP2LJvqm9oxKxnvoUbt+9ZrPvuK2vwm3f+Ge7ubjJkRkRERERERERERH2F+ZhoqR0UN5p3bfTy9MSI4UNlyoaISH6SJGH3wZO4nHtdiLu6uuD5ZfMxQBMhU2aieyVioV+0JlKmTGwnOkq8T7X1zahraIafr5dMGdm3n/7ytx0WNj67aDY+2vAzqHx9ZMiKiIiIiIjsUUNDA/bt24fNmzcjNzcXwNMVNAJAUlIS1qxZgylTpvROsk6GxY1ERHbMYiCvXP7ixqzsPKFwx8XFBaMSOKsAEfVt2qISfPblTuj1eiGeEDsMyxfOg1KplCkzUUGxeELIg0JAZ+Lp6YHQ4CCUVVS2xwqKy1nc2Ik3f/W/FoWNnp4e+MP/vIVvpiyXKSsiIiIiIiIiIiLqSyLCzSZ8LZW3uNFoNOJyVrYQG5UQD1dXnmJDRH2TJEnYezgDF65eE+IuShesWTYPg/r3kykzS+adG80LAZ1BaHAQPDzc0dra1h7TllbDb7D9PA/2IvN8Fv74yVdCTKlU4r//5btY//qLTjdWTkRERERE3bN//36kpqYiIyMDwNMXNCYmJiIlJQVJSUm9k6gT45FXZ2LNj+1HXmBEtmPtASGZ/k6tPZBlxesrIjxEWK6vb0BjYyN8fLyf7gasSt1yY0mScOFSlrBuxLCh8PY2n8XMyufKmvckyWTdvq1hxb4lh34ftuYPzZHvN9F9RSWl+HTrDrS1iYWNscMGY8Xi+VYUNvb868O8uNEZB/OA+/fr0eJGbXH50322WPMZYrL2+er999Jzl67idx99LsQGaCKx7c+/wZiEEU/3eFj1md1XPwM4SNqrZB2U5nPtUKx5T+LJD93gyJ8B1uTe0baO/FgQERERERH1DPMJX0vKyiDn76UbN2+jobFRiI0dnWB5RTmPj1o1Jirjvq263zIeh1dYM4mltXnL+Hdm1XlI1uzXim2t3recE5Za8dqy4XktkiQh/dgZnLuSh0efHBcXF6Qsm4fBg/pbsd+ePb+jTW9ASUWtEHPG8VClUglNZARu3nk4gWlBaTXin1TcaDI8fv2TSPonX6czMhxH1+sNeP2f/ks4H8fL0wM7P/p/mDttPADTU7xfWPk36qjjoXKOe1i1b/uYdLrLOM5ETk/OL4bWcNC8HfWzh4hIJrm5uXjllVeg0+kAPF1RY2xsLNasWYOFCxdCpVL1XrJOjsWNRER2zHwgDwBKysoxeNCA3k8GQFFxCcofKRYBOhnIIyLqI0pKy/HJlh3CrJgAMHzIIKxammQ3HRuB+z+sCkvE4sb+GuecvTNaE4lzl7LalwtK5O98bG8MBgNe/+nbMJkeDsh5eLgjPfVPGGLNIDQRERERERERERFRF0WEhwnLcnduvHDpirAc1S8SoaEhnVybiMh5SZKEgyfO4vTFq0JcqVTiuSVzMdTOxpTKqnUwmZ2IGhUZLmNGthMdFSkUNxabFXUSsOFPnyE7/5YQe/snr/69sJGIiIiIiAjIzs5GXV0dgPu/IR8taHy00FGj0SAlJQUpKSksaLQRFjcSEdkxX18f+Ph4o7GxqT1WUipfcWN27jVhOcDfDwMH2NfBaiKi3lJaVoG/bfkaLS2tQnxozEA8uywZLi4uMmXWMYPBiOYWsQgzLDRYpmxsK9zsJJP6hqZOrtl3vf+nT3A5W/xc//lPvsPCRiIiIiIiIiIiIup14WHiMd2Kyiro9Xq4ubn1ei6SJOHOvQIhNm70qF7Pg4hIbpIk4fCpczh1Tiz4ViqVWLVoNoYPHiBPYo/RYjYhrZenJzw9PGTKxrb8/MSTaVvarOiq6IRu3tHinfc/EmKjYgfjx6+ulikjIiIiIhtSKNgJGOBjQN2yevVqqNVqfPDBB8jNzQXwsGOjn58fVq9ejdWrV0Oj0ciZZp9gP61kiIioQ+bdG0vK5JuptL6+QVgeNnSwRctlIqK+oLyiCn9L/RrNzS1CfPDA/lj9zEK4utrfHCJ6g8Ei5uHuLkMmtududsKL3mCUKRP7dOdeId5+9/+EWNywGLzxvVdkyoiIiIiIiP4/e3cdJld5/g38O7bu7hbZZOPu7iEeoiQQoBBooUJLnV+B9qUUaCmlFNcEiLt7Nu6ebJLNurvbyHn/CNnkmdklyY6cmd3v57rmguc+Z865ZzI7O3vuuZ+HiIiIqC0zXrkRAPILCmXI5LZ7Z2UHgMBAf5kyISKST+Lxczh04rwQUygUmDlxJLrEt5MnqfvQGdUENRr7q9laikYt1kONH3tbJkkSnv/jP1B/T7OrQqHAJ2/+rlW/JoiIiIiIqGUmTpyIdevW4dSpU/j1r3+NiIgISJKEiooKZGZmIikp6f4HIbOxuZGIyM6FGs1UmpsvXyHPzc1VGDc0NDSzJxFR61VUXIKvV6xHTW2tEI+LicS8mZPtsrERABq0ps2NrbV4Yzybd1ONnW2VJEn42e/+ipoa8fX78Tt/gZOT7WdBJyIiIiIiIiIiIvL38zW5tp6bJ8+ErwqFAh7u7kKsqqpallyIiORy6MQ57D96WogpFArMmDgC3Tq3lymr+9PpDcJYrVLJlIn1qdXiY9Pq2dx4x/J1O7D3sPj6fWHJbPTr0VmmjIiIiIiIyBF4enrimWeewe7du7F792489dRTuHLlCl588UV07twZr776Ko4fPy53mq0WmxuJiOxcaIi4cmOejM2Nnh4ewriyqqqZPYmIWqfiklJ8vWI9qmtqhHhMVAQWzJpi0lRnT9r2Srtt+bGLVm7Yjh37Dgux556Yi8H9esqTEBEREREREREREbV5SqUSIUYTvuYVyNPcCAAeHkbNjdVsbiSituPo6YvYe/iUSXzquGHokdBRhowenFIpfhVSbzA0s6fj0xs1cqqU/BooABSVlOGl1/4txCJCg/C33zwrT0JEREREROSQIiMj8fLLL2P37t1Yu3Yt5syZg23btmHJkiUYMGAA/vWvf+HatWtyp9mq8K9aIiI7FxosNjfm5ttRIa+qppk9iYhan5LSMny9Yj0qjWZojooIw8LZ9t3YCAAatenMpNomVnNsDbRarTBu6rG3RaVl5fjln94UYqHBgfj7n34hU0ZEREREREREREREt4UGBwtjuVZuBJqY8LWSE74SUdtw/Mwl7DpougrFlLFD0btbJxkyejhOxqsZttJaKADodGI91EmjbmbPtuXlv/4HxaXlQuy/f30Jnh5uMmVERERERESOrkuXLnj99ddx8uRJfPHFFxgwYAA++eQTzJo1C+PHj8cXX3yBrKwsudN0ePyrlojIztlTcyNXbiSitqqsvAJfr1iPCqMvMESEheCxR6fByclJpswenEZt+tG/vqFBhkysr8HocbG58bbfvv5PFBQVC7H3/vY7eHt5ypQRERERERERERER0W2hIUY1URmbG7lyIxG1RafOX8WOA8dM4pNGDUbfHgkyZPTwjBv8auvqoNPpoG6iTuroqmpqhTHrocDew6fw9ZptQmzWpJGYNm6oTBkRERER2YhCefvW1vE5IBsYPHgwBg8eDADYsWMHVq5cibfeegtvv/02EhISMH/+fEyaNAkeRj0XdH/8CSYisnMhwYHC2J4KedXVNTAYDDJlQ0RkG+UVlfh6xXqUV1QK8bCQICyaMx3Ozvbf2AgAarUKri7OQiwvv1CmbKwrr0B8XF6ciROJx07j8+VrhdjUCSMxe8o4mTIiIiIiIiIiIiIiusukuTE/X6ZMuHIjEbU9Zy5ew9a9h03iE0YOxIDeXWXIqGUC/byEscFgQG4rrYdmZuUI42Cjx97W1NbW4fk//EOIeXm6473XXpIpIyIiIiIiau0mTpyIL7/8EqdOncKrr74KSZLwyiuvoF+/fnj66aexe/duuVN0KGxuJCKyc6HGzY0yXng1LuRJkoTq6hqZsiEisr6Kyip8vWI9SsvKhXhIUCAWz50BF6NmQXumUCgQGSr+TskwKnq1FhlZucLY+HG3NfX1DVj661eFmLubK97/+5+gUCjkSYqIiIiIiIiIiIjoHiFBXLmRiEgO5y9fx5Y9po2NY4f1x6A+3WXIqOXcXJwQ4OspxFpjPVSSJNN6aIifTNnYh//3/ldITssSYm/87nmEhwY1cw8iIiIiIiLL8PT0xLx587Bu3Trs3r0bL730EjIyMvDiiy+ic+fO+NWvfoXjx4/LnabdY3MjEZGdCw0WL7QVFZegoaFBllzc3FxNmiBYzCOi1qqqqhrfrFyPktIyIR4cGIDH582Aq6uLPImZITIsWBi3xmIeAKRnZQvjqLC2XbR68z+f4XpyqhD72x9/gaiIMJkyIiIiIiIiIiIiIhKZrNwoY3MjV24korbi4tWb2LgrEZIkCfFRg/tiaP+e8iRlpqgQf2HcGuuhpeUVJt/ViWrDzY2Xk27hrQ+XCbGBvbviucWzZMqIiIiIiIjaqsjISDzzzDPYvXs3du/ejaeeegpHjhzBkiVLMGDAALz66qtISkqSO027xOZGIiI7Z1zIA4CCwmIZMgGUSqXJTKUs5hFRa1RdXYOvV65HUXGpEA8M8MPj82bAzc1VpszMY9zkl5GVY1KsdHS1dXUmvyejwtruyo1JN1Pw9/c+EWJ9e3bBC08vlCkjIiIiIiIiIiIiIlOhIeLkfLn5+TJlYrpyY3VNDQwGg0zZEBFZx5Xrt7B+xwGTWuGIgb0xYlBvmbIyX1So2NyYnpndzJ6Oy7hh09XZCQE+Hs3s3boZDAY894c3odPpG2NqtQofv/l7KJX8aiwREREREcknMjISL7/8Mk6ePIm1a9diwoQJ2LZtG2bOnIkBAwbgX//6F7Kysu5/oDZCLXcCRET04/z9fKHRaKDVahtjufkFiAgPlSUfD3d3oaGRKzcSUWtTU1OLb1ZuQGFRiRD39/PBE/Nmwt3dTabMzBcVLjY3VlZVo6y8Ar4+3jJlZHmZ2bnCWKlUIizIv5m9WzeDwYDnfvMaGhrufoZQqVT4+J+vQaVSyZgZERERERERERER2R+FfKeWJIQGiZPU5RcUwaDXy9KYYLxyo8FgQE1NDTw8LNg4IpnbLGnGxIUG/f33+bEzmzNposKM15nZczWak7c5r0Mz/60lM54zc3+sZZsg08zEzbm7WQ9ZzglFH+5BX7uZirXb9t/9ef7hZ3Novx4YOaSveT+rD8XM9/gm3kuNV27Myc1HfX0DnJ2dzDuXHUlLzxTGUaF+UCgfoPanMLc+aMbvD7N/7zXtk+XrcfT0JSH2m6WPoVunOKud0yHY7GfYzs4tJ3Met6zPGZuA2w4zPqeY+35q1mdpM5j7Wdasn01zzi3ne4I5r5Mm7tvKJtwnIjJHly5d8Prrr+P111/H0aNHsWLFCnzyySf49NNPG/fJzs5GeHh4i89RVXW7v8Oi1zBtiM2N5NAUZv5hJ+9KRbJdTW2bHPXCiWSAAkBIUIDQrJGbl/9gfzSa9TdS03f29HRHbt7dbZWVlaYnMvdny5yCmoyFwNa2+pltmFPANPfCiYO+L9DDe4h/69raOixbvRH5RcV37ydJ8PP1wRPzZ5nM1mzXmnhPCvDxhquLE2rrGhpjFy4nYeTQAbbMzKouXk4SxmFBftA4aR7w3ua8J5n5O0Ay78skTfnyu3VIPHZaiP1y6WL06hpvwWKeOY+bvzfbDP7ObVvk+nKbQ5OpuCVrQc+RtdXHTUREREREZF2hIeLkfFqtFiWlZQjw97N5Lu7ublAoFELtr6qq2mG/GEREdK/ryWlYvWWPyYq0g/p0w5hh/c3+LpbcIkP8hfdwnV6P0+cvYciAPjJnZhl6vR4nzlwQYtGhbXOi15y8Qvz+zf8JsXbR4XjlF0/KlBERERGRTBRK+RqJ7QmfA3IQgwcPxuDBgwEAO3bswKpVq3D06FGMHTsWCQkJeOSRRzB37tyHvhY5evRoVFVVYdeuXYiIiLBG6lbFn2AiIgcQEizOVJqbXyBTJrdXbrwXV24kotairq4ey1ZtQG5+oRD39fHGE/NnwsvT8b+0oFQq0KldtBBLPHqy1TRn19c34Nipc0Ksc7sombKRV35BEV5+7R0hFh0Zhld/87xMGRERERERERERERE1Lzgo0KShJjcvX5ZclEol3N3chFhlFWuiROT4bqZkYNVm08bGAb26YPyIgQ7f2AgAbi5OSIgLE2KtqR568UoSyisqhVjvzjHyJCOzX776b1RUir+fP/r77+Dq4ixTRkRERERERA9n4sSJ+OKLL3Dq1Cm8+uqrkCQJb731Fvr164enn34aq1evblyR8cdUVlaioqICkiTBx8fH+olbAZsbiYgcQGiwOFNpbl5hM3tan6fRqmWVlSzkEZHjq6+vx/LVG5GTJzaPe3t54on5M+Ht5SlTZpY3rF83YZxXUIibt9LkScbCTp+/hNq6usaxQgEM6dtFxozk89L/vYWycrGw+b9//Bnu7m7N3IOIiIiIiIiIiIhIPhqNxmSVxtw8GSd8NaqJVrG5kYgc3K20LKzctAt6g16I9+2egImjBreKxsY7hvfpJIwzs3ORlpElUzaWdfDISWEcFx6IyGDbr3Ist827D2PNtv1CbPGsiRgztK9MGREREREREbWcp6cn5s2bh3Xr1mH37t146aWXkJGRgVdeeaWx0XH37t3N3v/jjz+2YbbWoZY7ASIiur9Qo5Ub8wrka240KeRx5UYicnANDQ34ds0mZOXkCXEvTw88MX8mfLy9ZMrMOjrGRiA4wBf5RaWNscSjJ9GxfayMWZlPkiQkHhWLeZ3bRyPQz1umjOSzY99hfL9+uxCbP2MSJo0ZJlNGRERERERERERERPcXEhyIwqLixnFuvnzNjZ4eHsi75/yVDzBDOhGRvUrNyMb3G3ZApxcbG3t364RHxg5FK+prBAAkxIXB39sDxeV337sPHj2J2OhIGbMyX25+IW7cShViw3p3lCkb+VRV1+CFV/4pxPx9vfHOKy/KlBEREREREZHlREZG4plnnsEzzzyDq1evYuvWrVi9ejWOHDkChUKBiRMnYtKkSUhISAAAfPrpp1i1apXMWZuPzY1ERA7AZOXGfDlXbvQQxizkEZEj02q1+G7NZmRk5QpxTw93PDF/Jvx8fQBJkic5K1EoFBjWvzvWbDvYGDt/+RrKyiscupEzLSMLmdniv+Pwfl1lykY+1dU1+Olv/ybEfLw98e5ffytTRkREREREREREREQPJjQ4GJeuJDWOc/PyZcuFKzcSUWuRnpWL79abNjb2SOiIqeOG/7BiY+uqhyqVSgzt3REb959tjJ09fxnTJ42Fr4/jToy6L/GoMPZwdUavTtEyZSOfV975FJk54meEd/78IgL9fWXKiIiIiIiIyDoSEhKQkJCAl19+GUePHsWOHTuwatUq7NixQ9hPuud7zh5GvR6OQil3AkREdH/GKzfKOUtpU4U8qZU1/hBR26DVavH92i1Iy8wW4u5urnh83gz4+7Xe4seAHp3hpLk7z4nBYMC6LTtlzMg8BoMBazaJf6z5+XiiS4e2V8x77Z8fmbym3/q/XyM4KECmjIiIiIiIiIiIiIgeTGiIOOFrXgEnfCUiMkdmdh6+XbcdWp1OiHfr1B7TJ4z4obHRVEOD1uG/BzKoeweoVXe/GqnT6/Hdmk0O+7hu3krDkRNnhNjgnu2hUatkykgepy9cw/tfrhZiowf3weOPTpIpIyIiIiIiItsYPHgwXn/9dSQlJeHdd99FeHh449+4zf1970i4ciMRkQMwLuTl5snX3GhcyNPr9aitrYWbm5tMGRERPTydToeV67chJT1TiLu5uuCJ+bMQGODfGNPr9SguKUNhcQkKi0pQVlGBAD9fDOjTAxqNxtapW4SbqzP69eiEI6cvN8ZOn7uE3t27oGe3BBkza5l9iceQavRvOaxvVyiVbWsul/OXk/DuR98IsWEDe+OphTNlyoiIiIiIiIiIiIjowYUG209N1GTC12qu3EhEjiU7twDL121Hg1YrxBM6xGHmpFFQKBSorKpGYXHp3VtJKQqLy1BTWwdfb09MHTcMcdERMj0C83i6u2BAt3Y4cv5mY+xK0k0cP3UOg/r3ljGzh1df34Blq9YLMY1ahWG94mXKSB46nQ7P/v5NGAyGxpizsxM+/PtvW8UXeYmIiIhaTKG8fWvr+BxQGzJx4kRMnDgRn376Kf75z3+2ir+J2NxIROQAggPF1ZbyCoogSZIsv4iMC3nA7dUb2dxIRI5Cr9dj1YZtSE5NF+JOThpMHD0c+YVFuHztRmMzY0lpmVAguePS1RuYN3My/Hx9bJS5ZT0yaiDOXr6J2rr6xtiKdVvQPi4GHu6O856eX1CEzTv2CrEAP2+MGNBdpozkodfr8eyvX4Ver2+MOTlp8NHbf2lzTZ5ERERERERERETkmFQqcfWp0rJymTJpYuXGSq7cSESOIze/CMvWbkN9QwMkSUJ9gxY1NbUI8PeFk0aNL1duQmFxGerq65s9Rml5JZat3Y5Rg/tg2IBeDvlFyemj+uDSzSxUVNc2xtZs2oFOHdvB18dbxswezsbte1BUXCrEpo7oCT9v0+/vtGbvfbEK56/cFGJ//vkSdIiNlCkjIiIiIiIieT3zzDPw8vLCX/7yF7lTMRu/5UpE5ABOnDkvjD3c3WS7cKxSqeDm5irEKqs4UykROQa9Xo8V67bizIUryC8oQkp6Ji5fu4GzF66gpKQM67buwtrNO5F47BSu3biFopLSJhsbASC/sAgff70CSTdv2fhRWIa3pzvmTB4hxCoqq7Bqw1aZMnp4BoMB36xcD61O1xhTKIDFM8bA2ckxV9VsqQ++WIHT568Isd///Cfo3DFOpoyIiIiIiIiIiIiIHpwkSVizUbw+Hd9BvuubTa3cKEmSTNkQET249Kwc/O29z3D+ynWcvZiEI6cu4PiZS8jOK4Rep8f5qzeQmZP/o42Nd0iShH1HTuP7DTtRW1tng+wty93VGQsnDxJitXV1+G7NJod5T795Kw0HDh8XYnHhgRjVt5NMGckjLTMXf/nnZ0KsS8dYvLz0MZkyIiIiIiIisg/z5s3D+PHj5U7DbGxuJCJyAMtWbRDGk8eNlCWPOzzcTYt5RET2TpIk/OuDL/D58lU4c/4yrt24hYzMHJSVVaBDXHSTK9PeT319A1as24rdB4402wRpz/r36IRu8bFC7PS5Szh++rw8CT2kbbsPIDU9U4iNHNAD7aPDZMpIHpnZefjz3/8jxOLbx+APP/+JTBkRERERERERERERPZzTZy/gutFkggsenSFPMjBduVGn06H+ARqBiIjkJEkSjp+5jCvXU5BXUIyKqmrodHr4+XihS3wcFMqWTaJ9IyUDHy9fh5y8QgtnbH3dOkSiX1exWf5K0k1s2blPpoweXGFRCb74drUQ06hVWPTIYCiVbedrn5Ik4ad/ehs1Rg22H7/5ezi1sQlviYiIiIiImrJgwYLG/6+qqpIxk5ZrO3/lEhE5qOs3U3Dq7EUhtnjuDHmS+YGnp1jMq6x0zF+CRNS2nDl/Gcmp6XBzvbv6rEqlRPcu8Sbva03x8vRAu9goBPj7mmw7cuIMvl6x3uHeDxUKBeZPHQ1XF2chvnzVBly6el2mrB7MwSMnsG33ASEW6OeNaWMGypOQTCRJwgu//3+oqq4R4h+9/X9wdnaSKSsiIiIiIiIiIiKih7N81TphHBcTjUH9+8iUjenKjQBrokRk/85cvIaqmhqMGNQbOp0OAODr7Ymu8e2abYbTqNUICw5Aj4QOGDusPxZMH4/RQ/pCoRAbIcsqqvDFik04c/Gaw6x6eMeccf3h5e4qxLbvOYg9B47IlNH9lZVX4D+ffI3yikohPnVETwT7e8mUlTxWbt6DHQfE1SufWzwTg/t2kykjIiIiIiIi+zJo0CC5UzCbWu4EiIjoxy03WrUxOCgAY0cOkSeZH3DlRiJyRNm5eQgK9EdggC9y8wpRWFyCdjFR8PLyFPbz8fZEoL8fAgP8bv/X3w8Bfr5w+aEBsKGhAVt27cfFK2LzX3pmNj7+eiXmTJ+I6Mhwmz0uc/l4eWDO5BH4Zt2uxpjBYMCn36zEc08uQEJ8Bxmza9rRk2excv1WIaZQAItmjGlzs3Ou27oHm3cdEGJPLZyFEYP7yZMQERERERERERERtU2Klq0GBgBarRbfr9koxBbNm2nSWGMVzTToqFUquLq4oPaeVaKqqqoQGOB/750tft4Hv7/BvPubwZx/F7MettkvBwed/92cnwNz+89s8CPYJDkb52R9vs1/jV5LTkNmTgEUCiVCAv2RW1CMbp07QKlUwtnZCYF+vgj0F2/eXh4mP9fxHdohMjwMa7bsQXVNbWNcpzdg8+7DyMwpxCNjh0Kj0Zj3fmSjn2t3N1c8NmUIPlq5R/hnWrdlJwySAeNHDTM3EYsqLinDfz75CsUlpUK8Q3QIRvXvCrR01UZzX2OSquX3beG5S8sq8MtX/y3EQoMC8PffPd/yXB6Gw74fmZm3LT6DNUum39fmPmbZnjMZP9/I+jqhtkPWPyBk4liTSJCdUCj4vgzwOaA2LyIiAtnZ2SgrK4OHx/0XfLE3bG4kO2DOLxJ+iHto5v7ilnX2NQf90GHGxUGDBCxfLRbyFsyeBrVG3pWY7r9yo7mvE3MKgWYW8sz4GTGvkNdWf7b4Pk62IUkSbqakQ6VSAVABCiA4MAD9+/QQGhkD/H3h5PTj77FOzs6YOWUCIsPDsGNfIvT6u+87VTU1+HrleowdMQSD+vUy86fL3IvnD75r/56dkZlbiP3HzjXGdDodPvziOzy58FH07tHFvFwsaG/iUazdtMMk/uikEWgfE2Hm0c14zpW2fy8tr6jEz//4dyEWFOCHt1751YN/ZjP7CzR8H28zHPYCoKPmTQ9Pxm9qyfolARkLeo76pQ4iIiIiIiKyO7v2JaKwqFiILZo3S6Zs7vJwdxeaGyuruHIjEdmvurp6pGbkNI7jYiIxe8o49OuZgKAAP3h6uD/Udxpio8KxdPFsrN68B5k5ecK281euI7egCPOmjYefj2czR7AvXdtHYsHkwfhu21EhvmHrbtTU1mHaxDHNrm5pSzl5Bfjvp9+grLxCiEcE++HZR0fbRY629Lu/f4CCIrHJ873XfglvL8f7oi4REREREZE1/ec//0F5eTkiIsz9Hq082NxIRGTHjhw/jbSMLCG2eN5MmbK5S2nUsMmVG4nI3mXl5KGquqZxHB4agl8sfQL+fr4tOp5CoUC/3t0RFhKEVRu3o7yisnGbwSBh1/7DyMzOxfSJYxpXfLRnCoUCsyYOR219PY6fvdoY1+v1+Hz5KmTnDseksSOgVsv350NtXR3Wb9mFw8dPm2ybMnogRg7sibbWMP3HN95Dbn6hEHv3r7+Fn6+3TBkRERERERERERERPbxlK9YK4wF9e6FDuziZsrmturrapJlRp9PLlA0R0f3dTM2AwXB3UlZPD3fMmjwazs4tnzzby9MDS+ZNxZ5DJ3Ds9EVhW35hMT5evhYzJ45Ep/YxLT6HLQ3pFY+augZs2CfWG3ftO4SU1AwsmjcDQcIKvbZjMBiw/9AxbNq+F1qdTtgW5OeFFxaMh5sD1J0t6dCJ8/jsO3FC+Kljh2L25FEyZURERERERGS/EhIS5E7BLGxuJCKyY8tXbRDGnePbo1d3eVfPqq6uxskz54SYjzebKIjIvl2/mSKMgwMDWtzYeK/wsBAsfWI+1m3ZheTUdGHbtRu3kF9QhHkzJiM4KMDsc1mbUqnAwmljodcZcOpiUmNckiRs33MQF64k4fF5MxEVEWbz3K7dSMa3qzaipKzcZNuE4f0wcUR/m+ckt2OnzuOjr1YKsQmjBmP+jEkyZURERERERERERET08CoqKrFx204htnj+bJmyuWvP/kTU1dULsYiwUJmyIUdXUVWD6ppaaHV6aLU6KJUKqNVqOGnU8PZ0h4sZzWdEdyQlpwnjuOhwsxob71CpVJgwcjAiQoOxcecBNDRoG7fV1zdgxcadGNKvB8YM7e8QqwqOG9QNSqUC6/acEuLJqen4f//8H2ZMHocRQ2z7WPILi7Bs5XqkpGWabAsL9MELCybA093VZvnYg/r6Biz9/d+FmLubK97/60sPtQIpEREREREROQY2NxIR2am6unqs2rBViC2eO1P2i3R79h9CXV2dEOvVo6tM2ZCjqq6pRUZOPjKz85FXWIJ6rRZarQ56vR4qlQoajRrOGg1CAv0QGR6MqLAguLu1rYv1ZFlJRs2NnTpYbsZnNzdXLHx0KhKPnsLBoychSXdXDywpLcNny1fjkXEj0LOb/c+KolIp8fjsCXB1cULiSXH21ZzcfLz1n08wYfQwm63i+GOrNQLAjPFDMW5oH6vnYW8aGrRY+pvXhNeaq6sL/vfmn2X/nEBERERERERERET0MNZu2i40EarVasybNU3GjIDMrGycu3BZiHXvmoCgoECZMiJHUlZRhYzs/Nu10JwCZOTko6Kqptn9FQogOMAXkWFBiAoNQlRYECJCA9nwSA9Fp9Mh2agxztKrKXaJb4fgAD+s2rwbBUUlwrYjpy4gO7cQj04ZAw93N4ue1xrGDOgKV2cnfLftqFBv02q1WL1xG85dvIIFj05DaLB13/e1Oh0OHj6BzTtMV2sEgJiwQPx0/ji4u7atFRsB4B8fLkNSsji58N9efhZR4SEyZURERERERETWxOZGIiI7tXXXfpSVVwixx+ZMlymb224X8i4JsW5dOiMmOkqmjMhRlJZX4vTFJKRl5SIzOx/FZRX3v5MRf19vRIUFISYyFH26xcPXy8MKmVJrVFRcgqKSUiFmyeZGAFAqlRg5dAAiwkKwdvMO1N7zRQytVosN2/YgMzsPk8YOt0lToDmUSgXmPjISPl4e2LLvOAwGQ+M2g8GA7XsO4syFyxg5ZCAG9OkBV1cXi+dQUVmFoyfP4OCRkyivqDTZ7uSkwbxHRmJgL/tvGLWGd/73JS4n3RRir/7mecRGR8iUEREREREREREREVHLLFu5ThhPGjcKAf5+MmVz+zr4tp17hZizsxPGjR4hU0bkCKqqa3Hs3BUcOXUJhSVlD3VfSQLyCkuRV1iKUxeuAwAUCgU6xkZgeP9u6NYxBiqV/a+GR/JKy8xBfX1D41ihUCC+XYzFzxPg74ufLJyJLXsScfGqWKtKy8rBR8vWYs6UsYiOsP+Vbgf37IgAH08s33IYxeVVwrbk1HT89e330bljOwwf3B9dO3eESqWy2LmLS8pw6PgpHD1xFlXV1U3uM7JfZ0wf1RdOGvuuLVtDUnIa3nj/KyHWt3snvLDkUXkSIiIiIiIiIqtre3/9EhE5iG9XbxTGI4YMQFRkuEzZNFfIc8b4MSPlSYjsnsEg4UZKBhJPnselpFswGKT73+lHFJeWo7i0HOeu3MTGXYfQvVM7DOvfHfFxUVypjH6U8aqNXp4eCAsNtsq52sdF47knF2LVhm3Izs0Xtp25cBk5eQWYO2MSfH28rXJ+S1EoFJgwvB8SOkRj2brdyM4vErYXFBZj1Yat2LhtN/r17o7hg/sjIsy8WTIlScKttAwkHj2JcxevQq/XN7lfh5gILJo5FgG+9v0cWktyagb++q+PhFiPLvH45bOLZMqIiIiIiIiIiIiIqGUys3Jw4PBxIbZ43myZsrntzLkLyM0Tr++PHDYYnp4tm3RTkiTWsVopSZKQlpWHQycv4MzlG9Dpmq5rtPTY11MycT0lEz5e7hjapysG90mAt6e7xc5BrUtScpowjggNttoKik5OGsycNBqRYSHYsf8o9Pq7Kw5WVdfg69VbMHZofwzq293u3/86xoTij8/OwMZ9p5F4Jslk+7Ubt3Dtxi34+nhj6MC+6N+7O/x8fVr0uBq0WtxMTkXisVO4fO2GsGLkvQJ8PLFo6lB0iGqbKxQaDAY89/t/oKFB2xhTqVT4+M3fWbTBlIiIiKhVUSgABSfFgZ3//UFEP47NjUREdqi4pBRbdx8QYovnzZQnmR+YU8iTJAml5RXIzM5DWXkltFottDodJEmCRqOBRq2Gp4cbIsNCEODvB37Edmy1dfU4dvYyDp28gIKi0vvfoQUMBgnnrybj/NVkBPn7Ylj/7hjUuwtcXZytcj5ybMbNjfHtY61aSPP28sSTC2dj575DOHX2orAtN78An3yzErMeGY8OVpgt1dIiQ4Pw26XzsSPxJHYmnhZWcQSA+oYGHD5+GoePn0ZURBhioyMRHRGGqIgwBAcF/GiBSavTITevABlZOcjIykFySjryCgqb3d/JSYOZ44dgaN/uUCrb5oUISZLw/Muvm8y8+8k7f4FGo5ExMyIiIiIiIiIiIqKH992ajUJzh5eXJ6ZOGitbPtXV1dh74JAQCwoMQP++vX/0fuUVlcjIyr19vTs7F9m5+aitrYNWp4NOp4dSqYBGo4GTRoMAP19ERYYiKjQYURGhCAkM4Kp8Duhacjo27T6MjJyCh7qfWqWCWq2GJElo0GqbbW66V1lFNbbsP4FtB0+hd5d2mDFuCHy9W9ZsS62TJEkmzY2d2sdY9ZwKhQL9enZBWHAgVm3aifLKuysfGgwG7Eo8jsycfEyfMAIudl7Dd3HSYN7EQejZKRrfbjlisoojAJSWlWPzjr3YvGMvPNzdEPVDPTQqIgwRYSFwcXGBRq2GUqWETquDVqdDcUkp0jNzfvjdkIO8/EKTWquxkf06Y9rIPnB2art1vy9XbUHiiXNC7Jc/mY9eXeNlyoiIiIiIiIhsgc2NRER2aPXG7dBq785C5uzshEenTZItn9uFvMNC7HYhr1eT+9fU1uHGrTRkZuciIysP6Vk5qKqueaBzubg4IyosGFHhIYgKD0GH2Ch4e7E44yjOXr6BlZv3oKq69r77qtVqRISFIDI8FF6eHtBo1FCrVNDp9dBqdaiorEJmVg6ycvOh0+maPU5BcSnWbj+InYknMX/qGPTq0sGSD4kcXGVlFbJy8oRYpw5xVj+vWq3GI+NHISo8FJt27BPe02tr6/Dtmk0YPqgfRg4dAKXSvr+0oFarMGX0IPTo3A7fbtiDzNymGxDvNCneodFoEBEWDA93d2jUaqgaf761KK+oRE5eQbOrMxqLj4vEwulj2uxqjXcsW70Jew+Js5i/8PRC9OvVVaaMiIiIiIiIiIiIiFpGkiQsW7lOiM2Z/ghcXFxkygjYsz8RdXX1QmzyhDEmE/kZDAZcu3ELx06dw620TJRXVP7ocQ0GCfX1Daivb0BlVTVSM7Iat2k0akSGhaBX104Y2Kc73N1cLfeAyOJq6+qxbkcijp65/KP7uTg7IzI89G4DVGQYAv39TGpCDQ0NyM7Nb6yxZGTlILeZBiiDwYDTl27i8o10zJ44FIN6dbb7VfHINrJzC0y+j2Ht5sY7wkODsHTxbKzduhe30rOEbdeSU5FfVIJ508YhONDfJvmYIz4mDH98dgZ2HrmAw2evo6auocn9qqprcPV6Mq5eT7bYuTtEh2DK8F5o30ZXa7yjoKgEv/3bf4VYdEQIXn3pJzJlRERERERERLbC5kYiIju0fNUGYTxt0lh4e3vJkwyAvQcOo66uTog1VchLz8xB4rFTOH3uMrQ/0oz2Y+rq6nEjJQM3UjIAAEqlAt06d8CIgb3RMS66za7WZe8qq2uwcvNenLt8o9l9nJ2d0LtHV7SLiUJURBhCgwN/dFW3O/R6PXLzC5GRlYNbaRk4e/4y6htMCwlV1bX4bMUW9OnaEXOmjIKnu5tZj4lah+vJqcLY2ckJMVERNjt/t4R4BAcGYNXGbSgqFlcyTTx2Clm5eZg9ZQLcHeD1GhkahN89twDXUzJx6NRFXExKgcHQ/IzCWq0WqUZFzIehUavQp1tHDO/fHdHhbbuQBwBFxaX49V/eFmIRYcH42x9+LlNGRERERERERERERC13/uIVXLkm1pUWz58tUzZAVnYOzl0QG9a6demMmOioxnFVdQ2OnTqHw8dPo9Domn9LabU6pKRnISU9C5t2HkDfHgkYPqgPoiPCLHJ8spxryen4dsNulJY33cyqVqnQq0cXDB/UD7HRkQ80uaWTkxNioyMRGx3ZGGtoaMC5S1eRePRkk3WWuvoGfLtxH85dScbCaaO5iiOZrNoY6O8Lfz8fm53fzdUFj82ahMTjZ3Hw+FlhRdKSsnJ89t0GTBk7DD26dLRZTi3l4qTB9FF9MWloT5y5mopDZ5KQnltknXM5azCgW3sM690JoYE+VjmHo3nptX+jtLxCiP3vjd/ebvyXfnzVSyIiIiIiInJsbG4kIrIzt1LTcfTkWSG2eO5MmbK5Xcg7e/6iELu3kNeg1eLM+StIPHYK6Zk5TR3CLAaDhAtXbuDClRsICvDD8AG9MLBPN7i5yjdrK4nOXr6OlZv2oqqm6dUaQ4ODMHxwf/Tv0x2uLZhtV6VSISIsBBFhIRjcvzcenTYRJ89cQOLRk8jNN11B7szlG7iemslVHAkAkHQzRRh3bBfzQE21lhQU6I9nFs/Dph17cSXpprAtJS0TH3+9AnOmT0JkeKhN82oJhUKBTu2i0KldFErLK3HkzGUcOX0ZFVUPtjrvgwjw88awft0xsFdneHCG6ka/efVtFJeUCbH//v3P8PRwZzGPiIiIiIiIiIiIHM7yVeuFcVRkOIYNHiBLLgaDAVt37BFiTk4ajB8zEgCQX1CEnfsO4fSFy9Dp9FbLQ6vT4diZizh25iKiI0IxeugA9O2RwNX5ZFZX34C12w82u1qjn68Phg/qh0H9e9++Zm8mJycnDOjTEwP69ERGVg4OHTuFk2cvQqvVCvtdTc7A3z74Do9OGopBvRLMPi85riSjyV5ttWrjvZRKJUYO7ouI0GCs3bYXtfesgqvV6bB+x35kZOdh0ujBUKvt/+uKTho1BvXogEE9OiA9pxCJZ67j4o30ZldzfFAKhQKRIf4Y0rMj+naNg4uTxkIZO74d+4/huw27hNj8aeMwadRgmTIiIiIiIiIiW7L/qwVERG3Mt2s2CWN/P19MHDtCllyaLuQ5YfyYkZAkCSfOXMC6LbtRVf1gTS2uri4IDQ6Es5MTNJrbF2l1Oh0atFoUFBajorLqR+9fUFSCNVv3YtOuREwcPRjjhg2ASnX/GS/JOnQ6Pb7bsAsnzl9tcnvXzh0xbuRQtI+LtmjR1dXFBSOGDMDwwf2RnJKOXfsPmTSM3VnFcWCvBCycPtbmzWxkH+rq6pGakSnEOnVsJ0suzs5OeHTaRESGh2LX/sMwGO42o1VUVuHL79Ziwuhh6N+7u8N8ScHX2xNTRg/CpBH9cSM1C+nZ+cjMLUB6dkGzsxY3JcjfB5FhQYgKC0ZMRDDiIsO4Sq+RvYnH8c0q8fPBrEfGYtrEUTJlRERERERERERERNRyOp0O363ZKMQemzPzgVa6s4Yz5y4gNy9fiI0cNgRubq7Ytf8wtuzaf9+mRmdnJ0SGhyIqIgxREWHw8/WBk0YDlUoFg8EAnU6HmppaZObkISMrBxlZOSgpLWv2eOlZufhyxQYcO3MBi2Y/Aj8fb0s8VHpIVdW1+OCbdcjIKTDZ5uzshJmPTMDQgX2s9tqNigjDY3OmY/rkcVi3eQeOnz4vbK+rb8DyDfuQX1iG6eMGOUyNiSynqLgURUaTY3ZqHytPMgDax0Zi6aLZWL1lD7LzxJ+bM5euIbegCHOnjoOPt6dMGT686LBALA4LhCQNQXFZFTLyipCRW4SM3GJk5BWjtpmGR4VCgZAAb0SF+CMyNABRIf6ICPaDMxsaTdTU1uFnf35biPl4e+LdV38pT0JERERERERkc2xuJCKyI5IkYfmqDUJs/qwpjY2Atnb2/MUmCnmDoTdI+PDL73H52s1m7nl7tb32sVGIighvLOIF+Ps2W1CRJAnlFZXIyMpBemY2MrJykJySjvoG0wvBDVotNu08iPOXr+PxOY8gLDjQvAdKD62hQYvPVmzGlRupJtvc3Vwxd+Yj6Nuzm1ULaAqFAh3axaB9XDROn7uEVRu2otpo9cjj566iqroWT8+fAicNP/a0Ncmp6dDr7zYRqlRKtI+Nki0fhUKBgX17IiwkCKs3bkdlVXXjNoPBgO17DiIzOxfTJo6Gk5OTbHk+LJVKhc7to9G5fXRjrKq6Fhk5BcgvKkGDVgetVgedXg+1WgUntRpOzk4IC/JHZGggXF2cZcze/tXW1uH5374uxLw8PfDe//uDTBkRERERERERERERmWfvwSPIyy8UYovnz5Yll5qaGuw7eFiIBQb4IyoqEv/84AukZWY3e9/w0GAMHdgPnTrEITDA74Ea3Lp07tj4/1XV1UjPyMaJsxdw7uJV6PWmDZRJN1Pxt3c/waxHxmJIv55sXrOhsooqvP/VWuQVlphsi+8Qh0VzpsPfz9cmuXi4u+Hx+bPQq3sXfLdmE8orxEkmdx85i5q6esyfMkK2JmGSR1JymjD29HBHWIi835/w8fbEk/OmYueB4zh14YqwLSe/EB8vX4tZk0ajQ5x8dduWUCgUCPD1RICvJ3p3vt1AKkkSausboNXqodXrYTAYoFGroFGr4eykgUbNSZgfxGvvfobUjBwh9tafXkBwoL9MGRERERE5GiWg4N+CAJ8DIkdmJ9/yl364kWzMvQAu8d/Ptsz59zLz30rOYkkbeJ2dOH0eySnpQmzxvFlo8b+5Gf9cNdXV2Lv/kBAL8PeDBCX++s//oba2rsn7+fp4Y9igvhjcvw+8PD0ePFWFAj7eXvDx9kL3Lp0AALV1dTh55iISj55ArlGBEwAysvPw5vtfYfKYIXayiqOMP5tmeNgiaH1DA/63bB1upmaZbOvRtTMWzJ76UP/25lIoFOjXuzs6to/F92s34+KVJGH75Rup+N+y9fjpohlw4iyIbUrSzVvCOC46Es7ONm6ka+LnKyoyHM89uRBrNu1Aaob4c3Q56SbyCoowb+ZkBPr7Cdv0ej1qa+tQU1uHmtpa6HR6hIUEwc3N1QqJm/ee5OHuioQO0UjoEH3/nS3KzM8pZt3dOp+n//bux0hOzRBib/zplwgPDTbvfHdPbKHjEFmTg35hi180owdlzt+6Zr3OzP0dwOsDD62pvB31sRAREVELNVUT5d8ORGQ5jtL4tnzlemHcp1d3dO7UsZm9rWvP/kSh7mmQDPD09sFb73/a5GqNKpUKvbt3wfDB/RAXE2XWc+7h7o4unTuiS+eOKJ9aiaMnz+Lw8dMoLSsX9qurb8B367bh7KVrdrKKo1zXI8ytATz4/SuqqvHvL1ajsLhMiDs7O2HWlAkYOrCvLD9v3RLi8cpvXsDqjdtx4sx5YduRM1cgSRIWTht9NzdzUzTrsoVc17zMO7VZZHi+bzc33j1xp/axUNj6S80K0wY+tUaFR8aNQGR4KDbvToRWq2vcVlunxbfrd2L4wN7o1inuh9qn6a26phaSJCE2Khz9e3ZputZv1tuR+a9RhUIBN1cXwBrlWquQ+zs1ps5fuYF/ffK9EBvWvyeemjfVcidpi9dgZf1MKOPrzJzHzedMhnPb33uSXTP3vcxB/la0K7LVcGUkGe6/T3P4M01ERGayk+ZGIiICgOWrNwrjDu1i0b9PT1ly2bP/EGrr7hbytFotSipr8O3aLU3u36ljO4wcMgBdOnWASmWZ2edcXVwwYkh/DB/cD8kp6Th49CTOXbxdlLlDp9dj065EnL9yA88snAF/H0+LnJuaptXp8Mm3m0waG500Gjw2Zzr69rLuao0/xtvLE0uXLMCpcxfx3epNaNBqG7fdTM3Cpyu2YOnCaVBzdsQ2QafT4eYtsVm8U4d2MmVjyt3dDYvnzcD+Q8dx6PhpYVtRSSk+/WYVYiLDUVNb29jMWFdXb3IcpVKJIQN6Y/igfrKt8kvWc/naTbz9wZdCbFDfHnjuibkyZURERERERERERERknqqqaqzbskOILZo7S5ZcsrJzcPb8pcaxXq9HTkExisprTfZVKBQYNWwgJowaBk8rTPLp7eWJSWNHYPyoobhw+RrWbNqBsvIKYZ+km6l4473P8NMl8xAXFWrxHOi2mto6/PertSaNjX4+3nhx6RMIDgyQJ7EfuLm54okFsxDfPhbLVm0QaudHz16Fq4szZo4f4jDNztRylVXVyMotEGKd2sfIk0wzuid0REhQAFZu3IniUrFxO/H4WSQa1Umbcis9C6cvXMXk0UPQsZ2tJ1Yla9Lr9Vj6uzeFlYudnDT46M3fcRVaIiIiIiKiNobNjUREdqKhoQEr128VYovmzpCl6JCdk4tzFy83juvqG5CenQdfP63Jvl6eHlj46LTG1RatQaFQoEO7GHRoF4PU9Ex8s3I98guKhH0ysvPwzsfL8fMn5yA0SN6CUmskSRKKSsvx9eptSMnIEba5urrgZ08vQlxMlEzZ3aVQKNC/dw/4+/nif58tFxp0r95Mw9drd+DJOZOhVLKY19qlZWSjvqGhcaxQKBDfPlbGjEwplUqMGTEYEWEhWL91N+rq7zYvNmi1uHEr9b7HMBgMOHTsNC5fu4lHxo1E+zgW9FoLg8GApb95FTrd3Zls1Wo1Pn7nVRbziIiIiIiIiIiIyGGt37ITNTV3mwdVKhUWzJlh8zwMBgO27dzbONbqtDh38RrCIiJN9g0K9MfieTPRzga1MJVKhd49uqJTx3ZYu3knjp08K2yvqa3Dfz77DksXz0LnDvZV93BUer0BeYXFyMjJR0Z2Hk6ev4a6+gZhn+DAALy49Ak7WDXzroH9esHFxRlfLF8N3T2NQXuPnoO/jxdGDOguY3ZkC9dvpQljZ2cNYiLD5EnmRwQF+OHZRbOxcecBXL2R0qJjlFVU4rsNO5DQIQ4TRw2ClxWazMn2Pvh6DU5duCrEfv+zx/n7jYiIiIiIqA1icyMRkZ3Yc/AoiktKhdiiuTNtnofBYMDWHXsbZ3isra3DqfNXENfOdMWz/r17YM6MSXB3c7NZfrHRkfjjr57Hlp37sefgEWEmyvKKKvzr0xV4ccmjiAoPsVlOrVFZeSVSM3N/KOLlIyMnH9U1prPUuru54hfPPYmIMPt6vtvFROFXP30K7330pZD32cs30C46DCMH9pIxO7KFpJu3hHFEWAg8PNxlyubHxXeIw9Il87Fqwzbk5he26BilZeVYvnojunbuiAmjhlpl1miyrY+/WYVjpy8Isd/8dAm6du4gU0ZERERERERERERE5tux94AwHj96GIKDAm2ex9nzF5GTmwdAAa1OhxNnLsDL2xdOTk6N+ygUCowZPhhTJo6Gk0Zj0/zcXF2xeO4M9O6WgG/XbBJWcWzQavHh12uwdPFsdImPs2lerUVZRSWOnL6EazfTkJVXCK1W1+y+Af6++OXzT8Lby9OGGT6Ynt0S8PTiufj0m5UwGAyN8fW7j6Bz+ygE+dtPMyZZXlJymjDuGBcNlUolTzL34ezshDlTx+HE2UvYdfAYDAbp/ndqwtWbKbiVnoXRQ/qhX88ETmrswDJz8vHntz4WYvHtovGHnz0hU0ZEREREREQkJzY3EhHZievJ4gx1Pbt1lmUlvLPnLyEn73Yhr76+AcfPXISfv79QyPP0cMdjc6ZbdbXGH6PRaDBzynj07NYZ36xYj/zCu6s4VtfU4v0vV+NXzyxAWDBXcHwYBoMBl6+nIPHEeVy9mXbf/Z2dnfCznyy2u8bGOyLCQvDTpxfhPx9/Lazgt3HXYXTpGItAf18ZsyNrkiQJ15PFVQ87dbDv4r6vjzeeXjQH23YfxNmLV+67v6urC+rrG4RCNQBcvnYDyanpGDNsEPr26irL6r9kvltpGfjD3/4txNrFROKVl56TJyEiIiIiIiIiIiIiC6mtrRPGXTvH2zyHmpoa7D1wCACg1+tx+twlNGj1CAoKatzH3c0VS59ciPax0TbP715dOnfEK795AV98uxpXkm42xnV6PT75dh1efGoe2seYrjZJpiRJwo2UTCSePI+L15IfqLnK28sTP392iV02Nt7Ro2tnLJ43A19/v64xptXqsHzjXvxyyUw2f7VSdXX1SM3IFmKd2tv3ancKhQID+3RHWHAgNuzYj5Ky203bLs7OcHN1afKWnJqJtKwc4Tj1DQ3Yvv8ILly9ganjhiGU3wtxOEUlZZiz9A+oqq4R4h/9/XdwdnZq5l5ERERE1CyF4vatreNzQOTQ2NxIRGQnggL8hXFFZZXNc7i3kKfVanHi7EUYJAnBwcGN+wT6++HFZ59AgB00hsVGR+LlF5/BB58vR2p6ZmO8urYO73+5Cr9+diEC/HzkS9BBVFRV4+jpSzh86gJKyiof+H7PPD4fMVERVszMfLHRkfjJ4/PwwWfLGmMNWh2Wr9+FXzw1l8W8Vio7Jw+VVdVCLN7Oi3kAoFarMW3SGHTu2A7pmdlw0mhuF+7cXH8o4N3+r6urC1QqFQoKi7Fl135kGBX06urqsXX3AVy4koSpE0YhOIgFPUdy7tI1TF7wnMnngI/e/gtcXV1kyoqIiIiIiIiIiIjIMjq0E6/X37yVZvMc9h44jNraOkgScPbSVZSUlSM+Ph5KpRLA7Ya2F599AmEhQfc5km24urrguScX4uvv1+H0+UuNca1Wh/99tQYv/3QxQlkLaJZWp8OR05eQeOI88gtLHuq+SxbOtou6+P0M6NMTaelZOHj0ZGPsVnoODp68iFEDe8iYGVlLclom9Pq7k6CqVEqHaXSOigjFi08vQF1dPZw0yh9dbXJIvx64cOUGdiUeR41Rc3xOfiE++XY9BvTqglGD+7IpzkGkZ+Vi4qJf4vqtdCH+1PypGDGot0xZERERERERkdyUcidARES3xbcXVxVLy8hGfX29TXPYe+AwautuF/IuXLmOyqpqREdHNxbyQoMD8dLPnrarAo6bmyt+/uwTJquylVdW46Pl66HV6WTKzP7V1Tdg1ZZ9+NNbH2PT7sMP1dg4YnB/JMS3t2J2ltOlUwcMG9RPiCWnZSPxxHl5EiKru2H0RYgAP18E+PvJk0wLdGgXg7Ejh2D44H7o26sbEuLbIyYqAkGB/vDwcG8s8AUF+uPJhbMxbeKYJpvesnLy8PHXK7D7wBE03LN6KdmvfYdOYOSMJcgvLBbij8+dhjHDB8qUFREREREREREREZHlGNdEryffsun5s3Nycfb8RQBARlYOCgqL4efnBy8vLwCAl6cHfvXTp+ymsfEOlUqFJQtno39vsVGtrr4eX6/aIjQ50V0Z2fl468NvsXrLvodubBw+uL/J69WeTX9kHPz9xDr+xj3HUFBcJk9CZFW30jKFcWxUuEM19ykUisYJXe+3X8+u8XjhyXno1dV0pV9JknD87GV88NVqJCWnWSlbspRL15IxZMazJo2NoUEBeOuPL8iUFREREREREdkDNjcSEdkJ41XFDAYDklPSm9nb8rJzcnH2wu2ZPrNz85FvVMgL8PfFz5cugbeXp81yelDOzk5YumQh2sVECfGc/CLs2H9cpqzs242UDLzx329w8Pi5ZoudLi7OiIuJamxuvcPfzxfTHxlnizQtZuYj4+Hn6yPENu46hEIW81ql1IwsYdyxfYw8idiAQqFA7x5d8MLTi9CjayeT7QaDhCMnzuJ/X3yHG8mpMmRID2rVxh2YvPA5k1VHuyd0xLt//Z1MWRERERERERERERFZVqeO7YRxcko6dDaarNRgMGDrjj2QJAk1tXW4dvMWlEolIiNvr3bm5uqKF599AkEB/jbJ52EplUosnjcD3RLEBp+M7DzsPnRCpqzsk1anw+bdh/H2x98iJ7+oyX2cnZwwdGBfLFn4KDQajbDN388XMxysHuri7IzFc2cIMa1Wh1XbEuVJiKxGkiSkZuYIMUdZtbGl3FxdMH3CSDw5bxoC/HxMtldUVWPFxl1YsXEXyiuqbJ8g3Vfi8XMY/uhzyMkvFOKRYcHY/f378PP1likzIiIiIiIisgdsbiQishOenh4ICwkWYkk3bTNTqSRJ2LZzHyRJQl19A65cFwt5Hu7u+Pmz9tnYeIezsxOef/oxhAaLs6juTDyOjOw8mbKyP3X1DVi5eQ/e+2I1ihW/ki0AAQAASURBVEvLm9wnKiIMi+fNxJt/+S2CAvxhMIjNj4vnzoCLs7Mt0rUYFxfTYl6DVocdB1nobW3q6+uRnSv+zMdFRzWzd+vh7u6GmY+MxxPzZ8K/iYJeWXklvlu7Bas2bENFJQt69ub9z77FgqUvo6FBK8RHDO6LAxu+gq8Pi3lERERERERERETUOsR3EJsbtVotUtMybHLucxcuISc3D5IEXLxyHXq9AWFhYXByur3a2VOL5iA8NPg+R5GXSqXC04vnIjgoQIhv23MYOXmFzdyrbUnPzsNbH36LHQdPwGCQTLaHBAVi7oxH8MYrv8HCR6ehqKgYWq14fX7R3OkOVw8FgI7tYzFicH8hdi05A3kPuWol2bey8kqUlVcKsdiocJmysa3oiFA8t3g2Rg/pB3UTqz4mJafhg69W49jpiybfcyD5rNu+HxMW/cKk8bRLxzgc2fApEjrGNnNPIiIiIiIiaivY3EhEZEeMV2+8fjPFJuc9e/4SsnNzIUnApas3oNPphELewkenIsDf1ya5mMPN1RVLFs4WVho0GCR8s3Y7tDaa8dWepWTk4I3/foPEExdMtqnVagzs2wu//flS/P6Xz2FQv15oaNDizPlLwn7DB/dHx/aOeWE5vkMchg3qJ8TOXExCTW2dTBmRNaRn5giFapVKiaiIUBkzsq3Y6Eg8/+RCjBwyACqV6Uf9q9dv4YPPv8WJMxdY0LMDkiThj//v3/jFH9+AJIlfsJg9ZRy2f/8xfLy9ZMqOiIiIiIiIiIiIyPL8/Xzh7yfWHa/bYMLXmpoa7Nl/ewW7jKwcFJeWwcXFBcHBt5sZhwzog4T49lbPwxKcNBo8Pm8mFApFY0yn1+ObNVuh17fta//HzlzGOx9/1+RqjRFhIfjF0iV45eUXMHLoALi6ukCv1+PQ8dPCfgP69ER8+zhbpWxx0x8ZBw93dyGWeOqyTNmQNaRkZAljdzdXBAX4yZSN7anVagwf2Bs/fWIO4ppo6mzQarHz4HF8snw9snMLZMiQ7vXhN2sxZ+kfUV/fIMSH9uuBxLUfISI0qJl7EhERERERUVvC5kYiIjsS30EsklxPtm0hLzs3HwVFJUIhr2/PbujZLcHqeVhKZHgoJo4ZLsRy8ouwff8xmTKyD5evp+A/zazW2D42Gn/+zc/w+PyZiLnn4v/xU+eEplCVSoXJ40baIl2rmTxupND8qtXpcfzsFRkzIktLNSrmRYSGNDZqtxVqtRojhw7A808uEH6m76ivb8D2PYn4bNlq5OaxoCcXnU6Hp3/5Ct5871OTbc8tmYcVn7wDFxfHmxWaiIiIiIiIiIiI6H46dRRXb0yyQXPj3gOHUVtbh9q6elz74XzR0dFQKpXw8/HGrKkTrJ6DJcVGR2LMiMFCLCM7D/uOnJIpI/ntO3oGy9fvNFmtUalUYsr4UfjdL5YivkOc0BR68ep1lFeIK+CNGjbQJvlai4uzM4YM6C3ETpxPQp1RYxE5rtSMbGEcGxUmvK7bCj9fbyx+dDJmTx4NdzdXk+15hcX47PuN2Lb3COrq6mXIsG2TJAn/984n+Nmf3jaZ5HX6+OHY+e178PXhJK9EREREFqFQ8nbnRkQOiz/BRER2xLi50RaFvH0Hj6C2rg56vQHXflgp8k4hz9PDHXNnTrZ6DpY2ccxwRISFCLFdiSdRUlYhU0byOnMpCR9/u9Fk9UqNRoM50yfjl88/iaAAf2GbwWDAoWNi8bNX9wR4eXpYPV9r8vbyRK/uYrPuoZMXTIqc5LhS0zOFcWx0pEyZyC/A3w9PzJ+JmY+Mg5uri8n2nLwCfPLNKuzYm2gyUyZZV01NLWY+8XN89f16k22v/+5FfPDmn6FSqWTIjIiIiIiIiIiIiMj6jFfFs/bKjTm5eTh7/iIAID0zG3q9AX5+fvDyut1U8djc6XB1Mb2Obu+mThiN4KAAIbbn0AnodHqZMpLProMnsHbbAZN4RFgIfv/L5zB5/Kgmr7snHj0pjGOjIxAVEWatNG1m6MB+QrNbXX0DTl+6IWNGZCmSJCE1I0eIxTYx2WlboVAo0K1ze7ywZA76dOtksl2SJJw8fwUffLUaV67fMmmyI+vQ6XRY+vs38bf3vjDZ9sxjM7D64zfg2kT9moiIiIiIiNouNjcSEdmR+Paxwvj6zRSrXlzNyc3DmR8KebkFhWho0MLX17exkLdg9lR4uLtb7fzWolarsXjeTGGFPoPBgCOnL8qYlTzOX72JL1dtg8FgEOLtY6Pxp1//FKOGDRSepzuSbt5CYXGJEBs+uL9Vc7WV4YPEx1FQXIbrKRkyZUOWVF1dg7yCIiEWF9N2mxuB2wW9Hl074YWfLEKvbp1NtkuShOOnL+CDz7+1SUM9AcUlZRg7+2ls3X1QiCuVSnzyz1fx55eWtsnZdYmIiIiIiIjaLknGGxHRPRQK826S9MC3JpsbH+L+wu0+JEnC1h17IEkS9AYDMrPzoFQqERl5u34wsG8vdO7Y3ipPqbVpNBosnjtDiFVW1eD85aT7PGfy/f5QKBQWvx06eQEbdx82OdeY4YPxu18sNZkU9478giJc/2Hy3ztaSz3U388HXTt3FGKJJy/98COjeIibGcx5PzGXue9nLc7bzNsDnKOguBTVtbXC3eKiI+V7zOb+e5m9KsvtJ8HV1RVTx4/A0/OnI8jfz+Q0ldU1WL1lL75bvxNl5VUP/Hw3f1OZdzPrtWLfamvrMGfpH/HZdxtNtv3fr57GR3//HdRqtY2zMphxa6uUZtzMJOfvD7PI9Jw59Hu4WSc340Y2JxlafjPvxGbezDl1C/++lftmzvPVwr/ZiYiI7mBzIzk4c/5IMe8COclAzouxZl0EePAP+J2MVm4sr6hEQWHhQx1D/IPhRzYZJGzdsReS4fZ+6Zm5QiGvQ7sY9OwmrnDnSCLDQzFkQB8hduTUxfvPVCrXH3dmaup96vqtDHy5cqtJg+zAvj3xi+eWmKzWeK8jx88I4/DQYLSLiTI7T3vQPi4aYSFBQuzIqYvyXxywyQWI1i0tM1sYO2k0CA8Nlikb++Lm5orpk8fiyYWzEODva7K9orIKK9Ztw/drt6C8ohLmfsZyhN+5Tf9ytO7PV0ZWDoZNXYzjZy4IcRcXZ6z78j38ZNGjD3QckpuZPx9ycthiIJEtyPdZ3iyyfjY08/cub/fciIiIiIiI2pZOHdoJ46QbyVY717kLl5CdkwsAyM0vRINWi7CwMDg5OQEAxo8earVz20JcTBTax0YLscQT52TKxvbOXr6OlZv3mMSnTx6L2dMmNrla4x3nLl0Rxu5ubujdvYvFc5SLcaNmdn4xCkvKZcqGLCU1Q6yH+nh5wtfHS6Zs7E9keAiWLp6FscMGQNNEA93N1Ax88NUqHDl1Hnp921vl1tpKSssx/rGfY+OuRCGuVCrxvzd+i1dfeobfuSMiIiIiIqImsbmRiMiOREWEwcXFWYhZayWta9dvNhbyyiurUFZegbCwMDg73z7/yCEDrHJeWzIu2FRUVePCtZsyZWNbxaXl+OS7jdAZXZAfOXQAFs2d8aOFPEmScDMlTYgNG9Sv1VxkVigUGGa0emNyWrZJEyg5ntT0TGEcFRH2o6/1tig6MhzPLZmP0cMGQq02fW6uJ6fig8+/xbFT50xWfCXzXLp6A4MnP4Yko1mgfX28sHv1Z5g2cZRMmRERERERERERERHZVrzRhK+FRcUoLS2zyrnOnr/U+P/pmTlwcXFBcPDtiRHj28ciJCjQKue1JeOaaHJaFrLzCmXKxnbKyivx7fpdJnM4PTptIiaMHn7f+6dn5gjj3j26QKPRWDJFWXXu2A5enh5CLCOnQKZsyFKMmxtjo8JlysR+qVQqDO3fEz99Yg46xJpO4KzV6bA78QQ+Wb4emdl5MmTYOmXm5GP4o8/dnlj6Hs7OTlj90Rt4bvEsmTIjIiIiIiIiR8DmRiIiO6JUKtEhLkaIXTdqgrCU/IK7Ba30rBxoNJrGQp63lye6d+lklfPaUnhocBMzlZ6XJxkbkiQJy9fvRF19gxAfMqAP5kyfDKXyx3/9l5aVo6q6RoglxLe3eJ5y6hwvzghcWV2DsooqmbIhS0lJE5sb42IiZcrEvqnVagwf3A8/fWoh2sWaPkcNDVrs3H8Yn3yzCtm5+TJk2PokHjuN4dMeR06e+KWBiLAQHNq8DEMG9JYpMyIiIiIiIiIiIiLbi4uJgtpoRa3rydaZ8FWlul0XuzPZq7e3d2OtzLgp0FH17NYZnh7uQqy1r94oSRK+3bALtXX1QnzK+FEYPXzwAx0jI0tsboyNirBYfvZAqVQiKiJMiLG50bEZDAakGTXlxkW3rtetJfn6eGHhzImYM2UsPN3dTbbnF5Xg8xWbsHlXImpr62TIsPW4eiMVQ2Y8g6s3UoW4t5cHdi5/DzMnjZQnMSIiIiIiInIYbG4kIrIznYxmKr2ebJ3mxuAfZiHV6vTIyS2A/p4V/oYO7NtqVjszLkreTM1EbkGRTNnYxqGTF3D9VoYQ69W9CxbMnvpAqy8aF/LcXF3h7+dr0RzlFujvB1cXFyHGYp5jK6+oRElZuRBrbUVoS/Pz9cGiOdMxe+p4uLu5mmzPKyjEZ8tXY9vug6g3apamB7d+6x5MmPsMyisqhXhCfDsc2bq81TWPExEREREREREREd2PRqNBO6PVtJJuWKe5MSbq9iR/dybzq6y8fa22tUz2Ctye1HDIgD5C7NT5KzAYDDJlZH0nzl3BFaMmmn69umPSuJEPdP/KqmqUGtWVjBsBWwM2N7YuOXmFqG8Qa3Yxka3vdWtJCoUCXeLb4WdL5qB/zy5Nfl/izKUk/Per1UhKTrN9gq3A0dMXMWzWUmTliu8vYcGBSFzzEYYP7CVTZkRERERtiELJ250bETks/gQTEdmZju1jhXHSDes0N8ZGR0KhUKCkrBx6gwEGgwFVVVVQKpUmxS9H1tRMpcazxbUmxaXlWL/joBDz8fbCojnT77ti4x3GzY2REaEP1BTpSBQKBaIiQoVYRjZXqHNkqeniqo2uLs4ICQ6UKRvHoVAo0C0hHi/8ZBH69uxqsl2SJJw8dxEffvm9yXsD3d9HX63EnKd/ZdIcOqR/LyRu+gaR4aHN3JOIiIiIiIiIiIiodYtvbzTh603rNDfGxUYDAMrKKgAANTU10Gq1GNivV6uZ7BUAhgzoK4zr6huQV1gsUzbWVVZeidVb9wsxL08PzJ05+YFrmsY1D41Gg+CgAIvlaC+MmxszcwthMEgyZUPmSs3IFsaB/r4m34Wgprm4OGPymKH4yYIZCAn0N9leXVOLFRt3YfOuRDQ0aGXI0DFt2pWIsfNfRGl5hRDv1D4aRzZ8gm6dOckrERERERERPRg2NxIR2RmTQp6VVm50dXVFWGgIKu5ZSaqiogKxURHw8fayyjnloFar0S0hXohl5LTOJjZJkrB83U7UG11sXzR3OlxdXZq5lynjYl50K5ylFAAiTWYqbZ2vi7YiJU1sbrzTwE0PxtXVBVMmjMLTix5FUKCfyfayigp8+f067Dt0XFjpl5omSRJefesD/PS3r5vMjD1t4ijsWv0Z/Hx95EmOiIiIiIiIiIiIyA7Et28njK3V3BgRHga1WoOKyqrGWEVFBdr/0PTYWvj7+ZjUeFvrxJ4rNu1BbV29EFv46DS4u7k98DGM66ERYSGtqtn1DuPmxrr6BhSVljezN9k74+bGuKgImTJxXOGhQXh20SxMGDEIThqNyfYzl5Lw0bK1yM7lKqf389n3GzHrmd+jrl58Px7YuysOrfsE0RGc5JWIiIiIiIgeHJsbiYjsTKcOYnNjanom6o0uBlpKXEwUyo0KeVGRra+Rzbho01oLeeeu3MD1lAwhNrh/byTEd3io42Tl5Alj4+evtTB+XNl5hTJlQuaSJAmpGVlCLJbFvBaJDA/F0ifmY+zwwdCoxYKeJElIPHYKn3+7BsUlpTJlaP/0ej2e+81reP2d/5ls+8niR7Hmi38/VMM5ERERERERERERUWvUqaPY3JhkpeZGlUoFHx9v6O+ZiK6ioqJV1r9Ma6J5zezpuLLzCnExSXyt9OvdHd27dHqo4xQVlwjjiLAQs3OzR95envBwF1f2KywpkycZMotOpzP5mY6NCpcpG8emVCoxqG93/GzJHMS3M210LymrwOcrNuLgsTMmk5jS7Zrx3977As/+9u8mz8+UsUOwZ8V/4e/rLVN2RERERERE5KjY3EhEZGc6tosRxgaDAckp6VY5V1xMNMrvWbmxuroaQYH+VjmXnIwLeQXFJairb5ApG+s5cOycMPbx9sLsqRMf+jjVNbXCOMDfdBW31iDQ6HFV19bJlAmZq7ikFJVV1UIsNprNjS2lUqkwdGAf/PSpBU2u3JqTV4CPvlqJ0+cvQZIkGTK0X7W1dZjz1K/w6bLVJtte+fXz+PidV6FWq2XIjIiIiIiIiIiIiMi+xLcXJ3xNTkmDTqezyrmcXZyFsU6rhYf7g6/y5yiMa6KZOa2vuTHxxHlh7OHuhrkzJj/0cRq0WmHs7uZqTlp2S6FQwM1NnHBRq7XOzxlZV2ZOPnT6u/92CoUCMa1w4mpb8vbywPzp4zF9wgiTVRwNBgn7j57Blys3o7SsQqYM7Y9er8cLf34H//fOJybbnpw3Fes+/QfcOMkrERERERERtQCbG4mI7IyXlydCg4OE2PXkFOucy9sLDcbFC73eKueSU3hoMJTKu7/yJOn2xf/WJCe/EMlp4sp1Mx8Z/9CrgxkMBuiNXgMaowv5rYXx49Lp9GzUclCp6eJr38vTA/5+vjJl03r4+njjifkzMXb4YOE9FAC0Oi227DqAFeu3orq6RqYM7UtpWTkmznsWG7bvFeIKhQIf/OMVvPa7F6BQKGTKjoiIiIiIiIiIiMi+xHcQmxu1Wi3S0jOtdDaVMHJ21qC0tMxK55KP8YSFmTkFrWrVsdq6epw8f1WIDRvUD+5uD9+oatzgp9G03okJNUaTLjboWt/3AdqC1IxsYRwaFAAXo8ZtengKhQK9usbjucWzEBEaZLI9MycfH36zFucvX2/z3yWoq6vHgp+9gg+/WWuy7Q8vPIHP3v4jJ3klIiIikotCASiUvPG7eUQOjc2NRER2qJNRMe/6Tes0N+bmF8LDw6NxrFKpUF7R+mad02g0CAsRL0S3tubGxBMXhLGnhzt6dU946OM0NVOnRq1qYk/Hp27icWlZzHNIKUZfdoiNimATmYUolUoMHdgHzyyag4AmGkavJ6fif19+jxu3UmXIzn5k5eRhxPQncOj4GSHu7OyE1Z+/i+efnC9TZkRERERERERERET2KcDfz2SiwqSbt6xyruraOmHSS28vT6SkpVvlXHIyXrmxQatFSStacezk+auob7i74uLtGkbfFh3LuOnTeJLH1kSlEmuien3raXhtS1KMJnuNi46QKZPWyc/XG0/Nn4aRg/pAqRTrzA1aLTbsPIjVm/egprZOpgzlVV5RhUmLfoE1W/cJcYVCgfdefwn/73fPsz5PREREREREZmm9V+eIiBxYx/axwthahbzyiip4eXk1jj3c3ZCWnmGVc8kt1Ki5sayySqZMLK+uvgEnzl0RYkMG9GnRrHjGF+oBwNBKZyBsamZFJS+4OxyDwYC0DLGYF8tinsWFhgRh6RPz0K9nN5Nt1TU1+G7tFmzddQBarbaJe7du127cwpBHFuLytZtC3NvLEztWfoJZU8bJlBkRERERERERERGRfYtvbzzhq3Vqog1arVATdXZywq2U1jdpn4eHu0lzSV19g0zZWJYkSUg8cV6IdUuIh6+Pd4uOZ1xH1bXiCVCNJ7d10rTOiW1bs/r6BuTkFwqx2KhwmbJpvZRKJUYO7oOn5k2Dn4+XyfarN1Px4ddrcCstq4l7t17XbqZi+KxncfD4WSHu5KTB9x/8FS8+OVemzIiIiIiIiKg1YXMjEZEdiu8gNjdeT7bOyo1anQ7e3ncLPiqVCiWlZSgtLbPK+eTk7OQkjFtTgerUhWvCLKUKhaLFs5Sq1WqToqeuidUcWwPjQp5CoYBKxY9Gjia/oAi1dfVCLDY6UqZsWjeNRoNHxo/EwtlT4O7mZrL91PlL+PjrlcjNK5AhO9uTJAkff70S/cfPRWZ2nrAtLCQIiZu+wYjB/WTKjoiIiIiIiIiIiMjWpIe+xXcwbm5MBgy6h7tJ0n1vWq1OaG5UqZRITcswWb3P0SkUCtOmPa0WkAxGt/s/Zz96a8G/9d1by2Rk5yO3oFiIjRjcv8XH02jE56k1T96o1YmP7aEmyFUoWn4zhznnlXMyW7PzbvqWnpULg0FqHKuUKkSFhza7v81vVnjMD35ulcVvEeFheO7xuejdLcHkfJXVtVi2djt27D8Cnc6M71EolC2/2ejfyiBJ+Pdn36P3xMW4lJQspO/p4Y7ty97D3GnjHzxvc3/3mPwue4ibw1LKeDODnO/hZp3XQZ8zc5n1fmT2yc24mXNaGV+j5GBs/3cPERFRW/bwSzoRkYMz9w8sR/3gLecflg//nHXq0E4YX7+ZAoNeB+Oms/tRKH/8Ip1Wq4WrqyvUajV0Oh1UytsXHlLT0uHr6/NQ57J3xgWqhgYtJIe+iHnX5eti82u3hHj4tfDf707R894CXm1dnTnp2a26erEhTqMxbexsdcxdhdOc58dK505JzxTG/r4+8PbyNO9crYYZ/14/cteO7ePw06dCsGnHPlxPFme2Liopw6fLV2P0sIEY3L83lEpbNwzb5nNOTl4BfvLLP2PHvsMm2+Lbx2LHyk8QHRlmlXM3fVeZ7uvQWvn7PVlOa/9sQNQWyfmZmIiIiIiIiASdjJobk25YZ+VGnV4vNDcqFUrU1dcjNy8f4WGhVjmnXNRqlVDn0+lbx4SvKZk5wjjA39ekOfZhuLq4COOSsvIWH8ue6fV6lJdXCjFXZ6dm9iZ7lZKeLYwjw0Og0WhkyqZtcHLSYNqEEegQF4nNuxJRUyt+Z+L4mUtISc/G7EfGIDjQX6YsrSc9KxdP/vI1HDh2xmRbSJA/ti17Dz27dJQhMyIiIiIiImqtuDwREZEdim8fK4zLyitQUFjczN5mkCQoFAqhmAcAt1LTLH8uu9N6mjkysvOFce8eXcw6np+vtzDOzs1vZk/Hlp0jrrTm582GOEeUmp4ljGOjI2TKpG1xd3fD/FmPYOqEUSbN4waDhD0Hj+HrFetRVl4hU4bWs3LDdnQbPq3JxsYBfbrj0OZlLWhsJCIiIiIiIiIiImp7TFZuTE5pZk/zqFUqODk5weWHhjbDDxPfpLTCmqheJzYzqlSt42tBmUb10Pax0WZNWhoWEiyMM7JymtnTseXmF0JrtLpcWHDra8Rq7VIzxebG2MhwmTJpezp3iMPzT8xBuxjTGnRBUQk+Wb4Ox05fgNRKJhOVJAlfrdyM7mMWNNnYmNAxFkc2fMbGRiIiIiIiIrK41nEVk4iolYmKCIOz0YyJ1ijmqdW3G1LuNDfqDbdXMkxNy2g1F1/v0GrFoo1xM46jKquoQkVVtRCLjjCvmBEVITbltNZinvHjigoLbmZPsld6vR7pxsW86EiZsml7FAoF+vTsiueWzEdYSJDJ9vTMHHz45fe4eCVJhuwsr6S0DAuX/gYLnv01SstMmzaXPjEPe9d+gQB/XxmyIyIiIiIiIiIiInI88e3bCeOCwiKUWmEFPY1aBeCemugPqxmmpKZb/FxykiTJpJGttdREM3LE5sbICPMmGTSepLCgsBi1dXXN7O24Mo3qof4+XvBwc5UpG2qJ6poa5BtNhB0bxeZGW/L0cMei2Y9g0ughjd+xuUOv12PngWNYtmYrKiqrZMrQMgqKSjDr6Zfx1Euvo9LoOygA8MtnFuLU1q/5+iMiIiKyRwoFb3duROSw2NxIRGSHVCoVOrYTV2+0RnOjk8aoufGHQl5NbS3y8lrXan0NWq0wNr7o7KgyjQp5Ls7OCAzwM+uYbba5Mdy0OYvsW1ZOnkmRPjaKKzfamr+fL55e9CiGD+prMktyfX0D1m3ZjbWbd6K21nG/FLBj3yF0Gz4dK9ZvM9kWGhyIrd9/hA/f/j+48QsBRERERERERERERA+sXWyUSc3u+s1bFj/PnUll79REGxpu1w0zsrKhNaohOrLqmlqTCWydNBqZsrGchgYtcgvE5i7jeubDCgsNhkqlEmKZWblmHdMepRvVQyPDAmXKhFoqLVP8N3TSaBAeyrq2rSkUCgzo3Q3PLpqFkKAAk+0p6Vn48Os1uHrDOisQW9uGHQfQbfR8bNx50GRbVHgI9q76EP969VdwdXWRITsiIiIiIiJqC9jcSERkp+Lbi82NSTctfxHU08MdAODs7AxnZ2eh4JWS1rpmKs0vKBLGnu5uMmViWenZecI4MjwUSqV5v96jwsViYE5eQasq7AK3m11z8wuFWCRXbnQ4qelZwjgkKIDNZTJRqVQYPXwQnlw4Cz7enibbL129gY++WoG0jKwm7m2/qqtr8PzLr2Hy/KUm7xkAMH/mZFxK3IhJY4bLkB0RERERERERERGRY9NoNIiLiRRi1pjwNSjwdiOKp6cnFAoFKiorAdye+DU9I9Pi55NLZrbYBKVWq+Dv4yVTNpaTlVcoNG0qFApEhoWYdUyNWo2wELFBrDVO+Goy2Sub4hxOilE9NDoi1KQxl2wnKMAPP1k4A4P79jCZ9LW2rg6rNu3Chu37UV/fIFOGD6e8ogpP/vJVzHr6ZRQWl5psXzJ3Ci7s+R6jhvSVITsiIiIiIiJqS9jcSERkpzoaNTfeSE61+Dkiw+8Wfby8vKDT6VBTUwsASEltPc2NOp0O2TlGTYBhraNwk5lTIIzNnaUUuN0gee+FeIPBgCyj58/RZefkwWAwNI4VCiAihDOVOprUdPELB7HRkc3sSbYSFRGG559ciB5dO5lsK6+oxNcrNmBf4jGTmaPt0dGT59Bz1Ex8/PVKk22+Pl747uN38N3H78DP18f2yRERERERERERERG1EvHt44TxdStM+BoVHgoAUKvVcHd3R1lFZeO21lQTTTda4S0iNKhVNEHl5IuT2IYEBcLJycns4xrXVW/csnw9Xk41NbUmNd6oVlIjb0tSM7KFcWxUuEyZ0B1qtRrjRw7C43OmwMvTw2T7+SvX8eXKTXbf4Lj/yGn0GLsAX6/earIt0N8X6z5/G1+8+xd4e5k+RiIiIiIiIiJLY3MjEZGd6tShnTBOunnL4ufw8fJsXL3R29sbwO3mEwBIz8yETqez+DnlkJtfCJ1eL8SiWskqfaXllcLYeIbRlnBxcUbwDzPY3nH6/CWzj2tPTp27KIyDA/zg4mx+EZRsR6fTIStXLMjGsbnRLjg7O2HmI+MwZ/pEuLo6C9skSULisdM4fPyMTNndX319A/74/97F8GmLcSvNdMbuiaOH4lLiJsyfOVmG7IiIiIiIiIiIiIhaF1vURKMiQhv/38vLC7W1dWjQagEAKWmtp7kx03iVvlZSD62rrxfGPt6eFjluXEyUML6SdBPFJWUWObY9OH76nFDvVyqViA5nc6MjaWjQoqSsQoixudF+xEaF4/nHH0WX+HYm2/IKirBm6x5hwmV7UVtbh1/95V8YM/d5ZGSbTnI9fcIIXNq3AjMmjrR9ckRERERERNRmsbmRiMhOxRut3JianoV6o8KNuRQKBaJ+WL3R0/N2EehOc6NOp0dGZpZFzyeXDKNCXoCvN9xcXWTKxrKMG1CdnZ2b2fPhdEuIF8bHT523+5kFH1RdfT2Onz4vxLp3Mi04kH2rra2DXi8WgyzR3EuW06VTBzz/5ELExZg2ne5NPIYrSTdlyOrHXbxyHQMmzMOb731qUmx0c3PF/976P2z9/mO+1oiIiIiIiIiIiIgsJL6D0cqNyZZfuTEsOBBq9e0VDL28vAAAFT/URPPyC1BdXW3xc8rBuCYaFRYiUyaWpdWKk9hqNBqLHLdX9wS43FNblSQJh0+ctsix5WYwGJB49JQQ65XQrtXUyNuKphrj3N1cZciEmuPq6oJHp4zFrMmj4Ww0mfLNlAzsPHBMpsyadvrCVfSZuBjvffa9yTYvT3d8+e5fsO7ztxEU4CdDdkRERETUYgolb3duROSw+BNMRGSnjJsbDQYDbqVmWPw8d5ob1Wo13N3dG5sbASAltXXMVGpcyIsMbx2zlAKAVmdczFNb5LjDBvWFQqFoHNfW1bWa1RtPn7uEurq7jcIKBTC0XzcZM6KWuPf1+WMxkpeXpwcWz52O8aOGmPz7rN+6G1k5prOBykGv1+Ot9z9D/wlzcfHqdZPtg/r2xPl96/Dckvl8nRERERERERERERFZkPHKjckp6SaTe5pLpVIhPPR2fdDd3R1KpRKlZeWN21PTLF+DtbXyikoUl5YJsahWUhPV6cXXg1qlsshxXZydMaBvTyF29MQZaC38+pPDjeRUFBQVC7Hh/VkPdTRN1aQkSZIhE/oxCoUC3RM64tnHZpk0EJ84ewknz12WKbO7tFodXvvXJxg09SkkJaeZbB81uC8u7PkeT8ydwlooERERERERyYLNjUREdsrLyxOhweLKUEk3LT9TaVR46D3n9EJpeQUaGm6v0JeS5vjNjQaDAVeu3RBi0a2kkAeYFi8sdaE5wN8PCfHthdjBIyccvlgiSRIOHjkhxLp0jIW/r7dMGVFLsZjnOBQKBQb3741xIwcLcZ1OjxXrtqKsvEKmzG67lZqBkdMW4/evv4OGBq2wTaNR440//wqJm5ehfVy0TBkSERERERERERERtV7GKzc2NDQgLSProY4hSRLKyitw4UoSDhw5iR37DmPTjn3YsusAdh04ikPHT8PPxwcAoFQq4enpicycvMa6QmuoiR49eVYYO2k0CA3ylykby1IpxWZGfROr2bXU8EH9hHFlVTXOX7pqsePL5eDRk8I4LMgf7aLCZMqGWor1UMfi7+eD+dMnQGXUgL193xHcTJGvif5mSgaGTH8Kr/3zE+j14uTZLi7OePe1l7B75QeIjght5ghERERERERE1meZ5Z2IiMgq4jvEIje/oHF8PfnBmxtr6+qRkZuLKzduISU9CxWVVdDp9NAbDJAkCSqVCmqVCi7OTlAAkHC7uTE3NxeZ2XloFxuF3Lx81NTUwM3NzfIPzkYuX7uBkntmXgWA+Hatp0FFoxZ/lVtyJtHhg/vjStLNxnFWTh5upWWgfazjPn+3UtORnZsvxIb37yFTNmQOpdJ0jg6DBYvZZHmD+vVCcUkZzly40hirqq7Bd2s34+nH5sDZ2cnmOe0+cASznngR1TU1Jtu6de6Ibz54Ez26drJ5XkRERERERERERERtRYC/H/x8fVByz6qD12+moH1cTLP3Ka+sws3UTFy9noK07DwUl5Q9VI3My8sLmZmZSM3IQmxUBG6lpEGSJIddrUqv1+Pw8dNCrE+3eJMGG0el0RjVQ7XaZvZ8eKEhQejQLgY3b6U1xnbuO4Re3RKgVjvmV6oys3Nx8UqSEBvWr5vDvr7bsqb+yQwGNjfas6iIUEyfMALrtu1rjEmShDVb9+Cp+dMRHGjbpvPikjKMmfs8soy+IwEAfbp3xjf/eQ2dO8TaNCciIiIiIiKipjjmlTgiojaiY/s4HDh8d5W5G8mpze5bV9+AY2cu4cyl68grLEFtXf1Dn8/DwwNKpRLXk1NQ39CAju1ikJaeiYTO8S3K3x4kGs1KGR0egqiw1rNyo1otFiWNVx0zR5dOHeDv64Pie4rJq9ZvxW9//qxDFvN0Oh1WrN8qxPx9vdC5fYw8CZFZOFOp41EoFJg8bgRKyyuQkpbZGC8oLMGaTTuwYPaUJptWraWisgoLnn3JpLFRoVDg5Reexmu/fUGWhksiIiIiIiIiIiKitqZTh3Y4evJM4/h6cgoemTBa2MdgMOBi0i1s33cYmTkFxod4KF5eXgCAa9eTcf1mCkKCAzFz2mTEREeZdVy5XL52A6VGk70OH9BLpmwsz8moubGq2nTCQnOMGNxfaG7Myc3Hjr2JmGL0GnQEOp0O36xYJ9TMnJ006N/Dcev9bVlTdTPWQ+1f94SOKC4tx8Fjd3+v1dc34Lv12/HMY7Pg4W67ycXf/fQ7k8ZGlUqFP/3iKfzp50+ZNI8TERERERERyYV/oZJlmDPDm6wX3lqet7mT2jnuBUfO5mdLnYxWGEy6mQwYxOa13IJibDtwEheuJkNv5ix9SqUSnp6eKC8vR2p6JlLTM5FTUIw/v/wLxMU4XjGvoKgYV68nC7FhA+xzlb6WzpTp4e4qjAuLii2RDoDbr4cRQwZg3ZadjbGsnDyHLeZt33MQOUYX7kcO7AWlUo73NXN+VmV8H5bzd5fRuRVNxCRJajpHzkRrN1QqFeZOn4TPv12DwqKSxvjNlHTs2HsIk8eNMPN19uD3/eir71FSKn7ZIy46El+9/waGDuzTglObuXKojR43ET0kc342+fvn4fH5ti1zP9vxOSciIiJqgSauX8n6ucoxa3RErVlbXVmtY/sYobnx2vUbkPQNAIDK6hrsO3IWh05dbNHkrk1xdXWFRqOBVquFwWBATm4+fvPKG+jVoxumTRqLbgmO1QjW1GSv0REhP3IPc9//bfs6NV7pLCevAHq93mIrU/bo2hlhocFCHXHH3kT06NoZkeGhFjmHrezYm4hso3romMG94OLi3LIDmvVSMed1IuN1KznroUZ5K5pqbmxiP4sw63GbkU8r/Sg8cnA/lJRV4NK1u99XKa+oxvcbdmLJ3Kk2aSosr6jCB1+tEmLx7aLxzX9eQ7+eXVp2ULPqoW20ntkWP9vJ+phtN5myCbket0LGxyznm7icrzNZn3O5yPj3gzm/e2T9t5Lzu4Ft9HcuERE5vLb4KYuIyGHEt48VxteT0yBJEgwGA05eTMLr/1mGv/33W5y9fNPsxsY77sxUeseNm7fwzn8/w+9fews79yVCr9db5Dy2cOjYKWHs5uqCPt0cqxh5P5GhQcI4IyvHoscfOXQAwkLFlS537E1EZnauRc9jbRlZOdi575AQCw8OwPD+9tnsSvfX1HVKx504oG1xcXHGwtlT4ObqIsRPnr2IE2cu2CSH2to6vPvh10Js9LCBOL9/XcsaG4mIiIiIiIiIiIioxYxrojeSU1FX34Bl63biD//4BLsOnXroxka1Wg2NuunmEYVCYVITraioQHZuPj784lu8/H9/x4XLVx/uQcgkKycP127cEmLDB7aeVRsBICpMrFXqdDrk5Jm3eue9VCoVHp83U1glz2Aw4JsV66DT6Sx2HmvLzM7Fjr2JQiws2B8ThveTKSMyV1MN7waDmZNtkk0oFApMnzASkUbvX9m5Bdiw44BN6tr/+3o1yiuqhJzWff52yxsbiYiIiMh+KZS83bkRkcPiyo1ERHbMuJBXVl6BqzdSsG7nMeTds+LV/fh6uSMiJACebi5Qq1VQqZTQ6w1o0OpQXlWDzNwiVNXUATBtbqyvr0d9fT0qAGzctge79h/G0icWoKNRbvamoLAYiUfF5sZBvbvASaORKSPriAoXZ121dHOjWq3G4/Nm4q3/fNJYKDEYDFi2cj1++/NnoW6mKGxPdDodvlm5Xij0KJVKLJ41AWq1ZWZ0JdtTNjFTKYt5jsPXxxvzZz2Cr1esh15/999tx95D8PP2Qod2MVY9/+ffrkV+YZEQe/P/fg0PD3ernpeIiIiIiIiIiIiITHVqHyeMr15Pxv/963NU/1C/bI5CoUBocCCiIsIQFRGOqIhQhIUEw8lJ01hHkCQJWp0OBYXFyMjKabyVlZaiuLi48VgVFRWQJAkKhQLVNbX4+KsV6NyxHZ5dsgDOTk6Wf9AWoNfrsWzleiHWGid7dXN1QYCfN4pKyhtjGVk5Fl1VMSoiDBNGD8P2PQcbY9m5+di2+wCmTRprsfNYS4NWi2Um9VAFFs8cx3qoA2uquZFTvToOtVqN+TMm4tNv16GsvLIxfuX6Lfj5eGLMsAFWO3dNbR3+/el3QmzmpJHo3MG+v+dDREREREREbZf9dyQQEbVh0ZFhcHZ2Qn19Q2PslXc+Q2ho84UapVKBhHaRiA0PQmRoIKJCA+Dp7vqj55EkCaUV1cjIKUR6TgGK8nOQX1TauL2iogKBgYEAbq929e+PvkTvHl2wZMFsu2xuMxgMWLZqPbRabWNMoQCGtsJV+oxnKq2orEJZeQV8vL2auUcLztFEMS8rJw+rN27H/FlTmiyq2AtJkrB643bk5OYL8QnD+yEyLKiZe5EjaLKYx5UbHUpURBhmTB6LtZt3NcYkScLqTTvw9GOPIjgowCrnbWhowNv//VyIjR85BH17dgX4GiIiIiIiIiIiIiKyudjoCGFcVFKK8oqqZuuQcTGRGD64P3p06Qxn5x9vPFQoFHDSaBARFoKIsBAM7t8bAFBaWoY/v/4m0jOzkV9YBL1ej+rqanh4eDTe99qNW/jdX/6BJx+bjR5dE8x8lJa3a/8hZGbnCrGRA3u1usleASAqLMSkuXHIgD4WPceksSNw8UoSsu+pK+7Ymwh/P1+Ln8uS9Ho9PvtmJbJy8oT4hGH9EMV6qMNTKpVC0yrroY7F3c0Vj82ahM++W4/6+rvfXzl04hz8fX3Qs6t1mtE//XY9CotLhdgfXnzKKuciIiIiIiIisgT760ghIqJGBoMBGrVaaG5saGhocl9vTzcM7d0Zg3t1go/nw608pVAo4OftAT9vD/TsHAttbQWOn72M9JxCZOcXC82Nd5y9cAXXrt/C0iX2t4rjgSMncCs1Q4gN698TQf6+MmVkPYH+vnBxdkLdPa+RjKwcizY3AreLeReuJAlNgoeOnYK7m6tdz1a6acdeHDomruAZHhyAiSOsNwsi2QabG1uHbgnxKC4pw4EjJxtjDVotvlu3Bc8smmOVlRS/XbPF5Msef/jlsxY/DxERERERERERERE9mHOXrgpjjUYDlUpcbc5Jo0H/Pj0wbFA/i6zY5+vrg4ROHRAY4Ifa2jpkZOdAr9OZ7Neg1eLjr1YgIb49lj65EBo7mfg1KycP23YfFGKhQf6YMHKgTBlZV1R4MM5evt44TsvIsvg51Go1Hp8/C/9472Ohmey7NZvg7OyEvj27Wfyc5tLr9fhmxXpcvnZDiIcF+2PiiP4yZUWWZFwTZT3U8QT6+2Hu1PH4dt02GAx3//02706Ej7cnYiLDLHq+hgYt3vlwmRCbOGow+nTvDK79SURERERERPZKKXcCRETUvOOnL6CqukaIBQSIK1nFRQbjmUfH4a8vLsTk4X0eurGxKXFREfBwc0WX9lEYPbA7YsP8EOhr2ixXW3d7FcfVG7eZfU5LKSgsxsZte4SYv683po8fJlNG1qVUKkxWILySdNPi51Gr1Xh83kyTQvKOvYnYsnOf3RVRJEnClp37sHNvohBXqZRYPGsC1GpVM/ckR9F0c6MMiZDZRgzpj24JHYVYeUUlvl+/VViB1xL0ej3efO8TITakf28MH9zPouchIiIiIiIiIiIioge3Yr1Ya4yIiBDqAEMG9MEbr/wGCx+dZpHGxjviYqIBAK6uLohvH4fRwwZg+uSxUKtM60hXryfj9bfeR21tncXO31J6vR7LVq6HXq9vjCkUCiyeNclumi8tLTpC/HfPzM41mcjQEiLDQzFzynghJkkSvvx2DY6dPGvx85lDq9Ph8+WrcercRSHu6uKEJY9OZD20lTCuid7beEuOo11MJCaPGSrE9Ho9Vm7cheKSMoue65s1W5GdVyDE/vhzrtpIRERERERE9o3NjUREduzjZWuEsb+/P9zdbzcvOjtpMH/yULz0xDT07BwLlcpyb+ntYiIa/1+tUiHYzxtPzRyJ8YN7NtlQtP/QcXy+fJXsDW4NWi2+XrHWpBlm0cwJcHF2kikr6+sYGyWMT569gNo6yxdWoyLCsGTBbJPXwLbdB7Bm03a7KaQYDAas3rgd23YfEOIKhQJLHp1k0gxKjkmhULCY10ooFApMnzQGkeEhQjw7Nx/rt+6x6O+WtZt34WZKuhD746+WNvm7jYiIiIiIiIiIiIis7+jpCzhw+IQQi4q6Xfvy9fHGC888jsfmTIebm6vFzx0XGyOMc3LzMGroQPz+V88jOjLcZP/iklK89tZ/UFVdbfFcHpTBYMCyVRtMGvvGDeuPmEjLNX7am/bR4fD29BBih46dssq5xgwfjAljhgsxSZKwbNUGbN9zwC7qURWVVfjw8+U4b7LqqRrPPzYd4cEBzdyTHI1xCUvu72RQy/Xt0QWD+nYXYrV1dfhu/Q6LNc7rdDr844OvhNiwAb0wtH9PixyfiIiIiIiIyFrY3EhEZKd2JZ7Erv1HhNidQl58bDj+tPRRDOuTYJWGDG8vT/j7eguxzOw8TB/TH795cjpCAnxM7nPm/GX87/Plsl1M1+v1+OyblUhNzxLiIwb2RMe4SFlyspVBfboKr4P6+gacPHPxR+7Rcn16dsWC2VNN4vsPHcd/P/3G4rMKPqzikjK8/+k3OHD4uMm2hdPHonfXjk3cixyV8fsfi3mOS61WY/7MR+DrI64SfPVGMvYdMv15bglJkvD3f4urNvbq1hkTx7TOlX2JiIiIiIiIiIiI7N3ZS0l4539fo76+XohHRUVhyIA++POvf4aE+PZWO39MdCSUyrtfm9Hr9cjIzEJYSBB+88JPmlzFsaKyCn99+7+yrOAoSRLWbNqOk2cuCPHQIH88MmawzfOxJZVKhaH9xKagk2cuWO3fYdrEMRgxZIBJfPOOffjnB58hr6DQKue9H0mScOrcRfz17feRdDNF2KZWq/Ds/CloFx0mS25kHfe+RwEAy6GObdzwgYhvFy3EikvLsHLTLmE13pZavWUPbqWJ35nhqo1EREREbYBSydudGxE5LP4EExHZocQT5/Hd+p0oKioS4nGxMZg/eShefGwy/H08rZpDXHSEML6VngkAiAkPwu+fmYVxg3qY3OdK0k1ZVnDU6/X48ru1uHzthhD39/XGtHGtv2nFz8cL3Tu1E2KJR09a7d9h6MC+mD9rikljWdLNFPy/f36Aw8dP2/w1IEkSDh07hb/987+4blTIUyiA+VNHY3CfrjbNiayPzY2ti7u7GxbOngoXZ2chfuj4aZy/dM3s42/dfQAXriQJsT/8kqs2EhEREREREREREcnhyvUUfL5iEzIyMoS4n58ffv7ck3hsznS4urpYNQdnZ2dEhImrHaakpgO43Uw3YfRw/ObFZ+Dh7ibsU1lVjTf+9T80NDRYNb97GQwGrFy/1WSVS41ajScenQyNWm2zXOQypF8PKJV3r+k3aLU4fvq8Vc6lUCgwd8ZkjB9tWmtOTc/CG//6ELv3H7bpKo4VlVX45OsV+PLbNaiuqRW2OTtp8LPFM5DQIbqZe5OjYj20dVEqlZj9yFiEBImrq6Zl5mDzrkSz/n0NBgP+/v6XQqxP984YP2Jgi49JREREREREZCtsbiQisjNnL1/Hys37kJmZKcSdnJzw2q+WWG21RmNx0eHCOCM7D1qtFsDtItmMsQPw2JThJrmcvXAFK9dvtXp+dzRotfj4q+9x9sJlIe7q4oxnF06Di7OTzXKR0/CBPYVxbn4BklPSrXe+wf2xZMFsk5ki6+rr8d2aTXj/k69ttopjcUkZ/vPJ1/h+7WbU14tFZKVSiSWPTsKw/qbNuOT4WMxrfQID/DB3+iST95bNu/YjLSOrmXvdnyRJeOPdj4VYpw5xmDVlXIuPSUREREREREREREQtU1hcio+Wr4MkwaS5cdojE9C3Zzeb5RIXGyOMU9LE+lpURBhe+tnT8PH2EuLFpWV47+OvbVKb0Gq1+HrFOiQePSnElUolnlk4HVHhIVbPwR74eHmgR0IHIXbgyHHodDqrnE+hUGDG5HGY+ch4k5qUTqfD+q278M8PPsO1G8lWbXKsq6vHwSMn8de338eFy6aTQXq6u+IXT85Cx9iIJu5Njk4B8bVny4Zasg4nJw0WzpwITw93IX7+ynUcOXm+xcfdvDsRl5NuCbE//vwpTvRKREREREREDoHNjUREdqSkrAJfr94BwLSQN3pIfyS0i7JZLjERYcJFTp1Oj8ycfGGfwb064alZY6A0uhiaePQkLl0VV8eyhvzCIvz7wy9NVmx00qjx/OKZiAgNsnoO9iI+LhpB/r5CbOP23VYtbvTr3R0vPLPYpJgL3F7F8bW3/oOvv1+H1PRMixd2JUlCanomvvp+LV576z8mqzUCtwucLzwxE327d7Loucl+3Ds7LwAY2NzYKsTFROKRcSOEmF6vx8oN21FeUdmiYx44chLHT18QYr//xTMmTZREREREREREREREZF0Gg4TPvt8Evd6AmpoaFBYWCtsXz59j03ziYsWV7vLyC1BdXS3EQoIC8cvnn4SXp4cQT03PxOHjp62aX2p6Jt5490OcOntRiCsUwJI5k9E1Ps6q57c3wwf0EsaFRSXYvuegVc85btRQvPTTpxAU4G+yLTU9C+9/8g1ef/t97Es8ihqjFRXNkZNXgJXrtuCPf30HK9dvMVmtEQB6demAP72wCNFtpMG1LTKuh3Ky19bBy9MDC2ZMhEYjrrq79/BJFBSVPPTxJEnCG//5QogldIzD9AkjmrkHERERERERkX3ht3mJiOyEJEn4cvU26PR6GAwGk5UbZ00eZdN8XF1dEBYSKMRS0k1XzeqdEIdFU00viH757VrU1dVbJTeDwYC9B4/gjX/+z2QlLyeNGksfm4F2RitPtnZKpQLDBoirE6akZWL/oWNWPW+nDu3wym9ewKD+vU226XQ6nDhzHm+//yne/PdHOHLiDBoaGpo4yoNraGjAkRNn8Oa/P8Lb73+Kk2cuNDkb6+A+XfHnFx9HfJztGoLJ9rhyY+vVp0dXDOzTU4jV1tXhpNGXNx7UG+9+JIxjosKxYNYjLU2PiIiIiIiIiIiIiFrowLEzjROqZmWJdT53dzcMGdTfpvmEh4XCyUkjxFLTMkz2Cwrwx4vPPgFXVxchvmrDthZPzPdjtFot1m3ZiXf++xnyC4qEbSqVEk/Pm4a+3Ttb/Lz2rmNsJCLDgoXYzn2HkJGVY9XztouNxh9feh5jhg9uchW0gsJirNm0A3/46ztYtmoDzpy/jMKikoeqXWl1OqRnZiPx6Cm8+78v8Ld3/ouDR0+irt605u7h5oqn507GT+ZNhqe7m1mPjeybST1UpjzI8kKDAxATGSbEJElCntF7/oPYe+gkTp2/KsR+/8ISTvRKREREREREDkN9/11sQJJu38g8TVxAdQjm5M3XDdlAU8WJh/GgL9PDpy8iJf120aWgoMCkCWziqCFm5dEScVHhyM4taBynZGQ3ud+AHh1RWVOL9XtONMbq6uvx1fdr8dyTCy2aU35hEZatXI+UtEyTba4uzvjp4zMRF2VOY6M57yvmvFbMfw8f1q8nEo+fR2FJWWNs0/a96JoQj+DAALOP3xxXVxcsnjsDvbt3wberN6KsvMJkn8zsXHy7eiNWbdiGyPAQREWEISoiHFERYQgJCmjyorrBYEBeQREyMrORkZ2DjKwcZGbnQavVNpuLj5cHHpsxDgkdYiz5EO2MnL/77OuzxgM3N5r7eUGuz1jmnteBPydptVrk5BWYxNVq1UMf68SZC9ibeFyIvfzC09BoNE3sbcZzZu7z7cD/Xi1nX+8pDsFR/+YjotapTf7uMlNTzxmfRyIiInLUzwNm/40q17V4otZO3lrV/RQWl2L99gON44wMsYlwzMjhcHZ2tnoe91KpVIiJisKN5FuNsVupaejaxbRxMDw0GI89Oh2fLVvZGNPr9fjoy+/w258/a3ZdGbhd67h5Kw3fr9ts0tQIABqNGs8+NgNdOsSafS5ZmPn9EIVCgUUzJ+AfHy6HwWAAcLum+M3K9fj9L5ZCrbbe16CcnJwwe9pE9OzWGctWbkBBUbHJPlqtFsdOnsWxk2cB3K6jRoWHIioiHIEBftBoNNCo1ZAkCVqtFrX19cjJzUdGVg5y8gqg1+vvm0evLh0wb8pI+29qNOvf2tyTm3EAO/sOk80me5Wt/iDn5zpDy+9q5r+DJEnYffA4bqaIvwednZ3QLibyoY9nvGpjbFQ45k8fb1aO9ANHrdk7dE1RpqZcOZ8zhZyNyHI+bkd+ndLDkekakGTG73pAxp9Nc79/JNNnHEVT93XQ654OScH3VQC8bkzk2OyjuZGIqI0rKavAmi37G8fGhbyundojwmgGSltoFxOJQyfONY5z84tQU1MLNzdXk31HD+yOyzczcDM9tzF28UoSLl1NQreETmbnkldQiMSjp3Dk+Glom1ipL8DPB88smIqI0CCzz+WonJw0WDRrIt79bEVjTKvTYdnK9Xjpp09bfVa+Lp064JXfvIAN23bj2Mmz0DVRfNNqtUhJyxSaUzUaDTzc3aDRqKFWqaDT66HV6lBVXfOjjYz3UqtUGNi7C2aMHwpXF9sWvUk+XLmxddLr9Vi1cQcyssVZln29vTGgd49m7tW8v//7E2EcEhSAJxfMMitHIiIiIiIiIiIiIno4BoOEz77fCP09DWmZmeJkppPGj5EjNcTFRgvNjSmp6ZB+aKQz1rtHF/S+2BVnL1xujKVnZuPw8dMYNqhfi3Ooq6/HqbMXkXj0JLJz85vcJyo8BI8/OhlhwQGO2xxvAZFhwZg4YgC27T/WGMvJzcf2PQcxdaL1X0PtYqPxx1//FMdOnkPi0ZPIzTedrPGO2to6XE9OxfXkVLPOqVAo0C0+FiMG9ECndlFmHYsci/H70J2mXnJsh0+ew9HTF4SYWq3GghkT4d7E93F+zNFTF3Dg2Bkh9tufPm7VZm8iIiIiIiIiS+NfsUREMpMkCV+t3iY0ghk3N8qxaiMARIQGQaNRQ6u93UwoSRJSM3PQJb6dyb5KhQKLpo7A//t4DRq0d5sPv/x2Dd545WW4tKDhTK/X4+KVJCQePfmjBZ+Rg3ph2rhhcHZqahWutqVDbCRGDuqNA8fONsZS0jKxa/8hTBwzwurnd3V1wYLZUzFl/CgcPXUWh46dRklp2Y/eR6vVorSsvEXn8/PxwrB+3TCod1d4etj57KRkcUqjGbJYzHN8BoMB67fuxs2UNCHu6eGOx+dNb7K5/sdcunoDm3bsE2Iv/fTJFv1OIiIiIiIiIiIiIqKWO33hKjJz7jbtFRYWoq6uTthn4rjRtk4LANAuNkYYl1dUoKSkFP7+fk3uP2/mI7iRnIqq6urG2OqN29C3Vze4urg88Hm1Oh2yc/Jw4swFnDh9HnX19U3up1apMHnMEIwb1h8qlZwr+9iPiSMH4eK1ZGTlFTbGduxNRHhoCHr36GL18ztpNBgxpD+GD+6H5JR0JB47iXMXr1q8VuXh7oohfbpiSN+u8PfxsuixyTEolZzstbU5feEK9h46KcSUSiXmTBmLmMiwhz7e39//UhiHhQRiydypZuVIREREREREZGtsbiQikllqZg5upWc3jmtqalBYWCjsM2m0PM2NarUa0RGhSE69O2tqSnpWk82NABDg64UZYwZg1Y4jjbG6+gZ8t3YTFsyeet9iXoNWi+ycPGRk5SIjKxtXryejvKKy2f0DfL2xaNZEdIiNeMhH1rpNHz8MV66noLCkrDG2afteeHl6YnD/3jbJwdPTAxNGD8e4kUNxJekmEo+exNXryRYptigUQOf2MRg+oAe6dIix+oqUZL+4cmPrIkkStu0+iMtJN4W4q4sLFs+ZDl8f74c+5t///bEw9vP1xnNPzDMrTyIiIiIiIiIiIiJ6eDsPHhfGxpO9dorvgJhoeVakCwjwh6eHByqrqhpjKWnpzTY3enq4Y/6sKfhs2crGmE6nxzvvf4a+vbohOjIMvj4+cNKooVaroTcYoNPqUFNbi6ycPGRk5SA9Mxs5eQXQ3zMBblOE1RqpkVqtwuLZk/CPD5c3NhRKkoQvv1sDFxcnJMR3sEkeCoUCHdrFoEO7GJRXVOLYqXNITklDRlYOqqprWnTMIH8fRIYFoXunOPRIaA+NSmXhrMmRsB7aulxOSsbWPYdN4tMnjER8+5iHPt75y9exda94vF8vXQRnZ6eWpkhEREREREQkCzY3EhHJbM/h08I4MzNTGHu4u2FIv542zEgUFxUuNjdmZP/I3sCwvgk4dy0FN9NzG2Onz13C6XOXEBToj7CQYDg7OUGjUUOhUECr1aKhQYv8wiLk5hc+0GyWCoUCwwf0xPTxXK2xKc5OTlg8exLe/ex73Fvb+Hb1RjhpNOjbq5vNclEqleiWEI9uCfGorKpGRmY2MrJzfmhgzXmgFRt9vT0RFRaEyLBgRIUFITo8BB7uD7d6G7VORrU8sJbn2PYdOo7T5y8LMSeNBo89OhVBgf4Pfbybt9KwauMOIfbzZxfDw8PdrDyJiIiIiIiIiIiI6OFkZOcht6BIiOXk5AjjSePG2DIlgUKhQFxsNC5cutIYS0lNQ78+vZq9T+8eXdD1dEdcvnajMZabX4DNO/ZaJKcAPx+MGtwHwwf04mqNzYgMC8aUMUOwafehxpher8fHX36Pnzw+D90S4m2aj7eXJyaOGQ6MGQ5JklBaVo6MrBxkZOUgMzsXxaVlyC8oMmlOCw7wxeA+XREVHoTI0CC4ujiLB2YBrE0zbW6UKREy282UdKzbttfkPWDS6CHo0aVji45pvGqjv683nl00q8U5EhEREREREcmFzY1ERDKqrKrBpWu3hFhWltjcOGZofzjJ2MAXFy2uilhaVoHSsgr4+ng1ub9SocDMsQPx1ufrTbYVFBajoLC4xbl4uLliUJ+uGNqvBwL8Hn4Fr7akfUwEZkwYgfU7DjbG7sxWWldfj6ED+9o8J08Pd3Tp3BFdOt+9MF9ZWYWLV69j3ZadqK2tE/aPCA3E0semwc+76dcaEWcqbT2OnjyLQ8fEZn+VSoX5Mx9BRFhIi475j/c/ExrmPdzd8MLTj5mVJxERERERERERERE9vH2HTwnjuro65ObmCrFJ4+VrbgSAuBixuTE1LQMGgwFKZfONhaOHDRKaG82lUABd49th+MBe6Nw+Fkql4v53auMmjBiA0vIKHDp5oTGm1enw8Vff4/H5M9G/dw9Z8lIoFPDz9YGfrw96dkvAxStJ+HzZKpNaVt9u8Xji0Yn8t6ZmKSC+Nh5ksmiyPxlZuVi1eTcMBvE9YOTgvhjQu2WTUyclp2HNVrGh/hc/WQB3N04UTURERERERI6HzY1ERDI6cuoCDPcUMAwGA3KyxZURJ44eYuu0BMGB/nB3c0V1TW1jLCUjC318Epq9T3RYIKLDApGeU2iRHGIjQzF8QE/06tIRGg1/dT2occP6o7qmDrsSTzTGJEnCd2s2oai4BI9MGA2NWt7nMzkto8nGxpiIELy4ZDZcnJ1kyozsnSRJJgVgFvMc09kLV7Br/xEhplAo8OjUCYiLiWzRMTOzc7Fs1SYh9vxTC+Dn69PSNImIiIiIiIiIiIioBWpq63D6UpIQy8nOFq7xu7m5YdiQgbZOTRAXGyOM6+rrkZuXj/Cw0Gbv07F9LIIC/c2a3BUAgvx90bNrRwzr3xP+vpzg9WEoFArMmzoWtXUNOH3xWmPcYDDgq+/W4ur1ZMydPhluMjX71NXVY/3WXTh07JTJtq4dY/H47PFsbKRmFRQWo7K6WohxslfHk1dQhO/Wb4dWqxPiA3p3w4hBfVp83Lc++Fp4PXh5uuOFJ+e1+HhERERE5MAUCkDR/ORMbYaCf18TOTJ2iBARycRgMGD/sXNCrLCwEFXVNUJs4qjBtkzLhEKhQFx0OC5dS26MXb2Rgp5d4qFSqZq937C+CUjfdLDZ7c2fDwgK8ENUWDCiwoMRHxeF8JDAFuVOwPTxw9Cg1eLAsbNCfNf+w7h09QYenz8T0ZHhNs+rqroaq9Zvw+nzl0y2RYYF4aeLZ7KxkZolSRJ2HziCyqrq++9Mdu3q9WRs3rnfJD5twmh07tiuxcd9+79fQKvVNo5dXJzx0nNLWnw8IiIiIiIiIiIiImqZY2cumUxOWFFRLoxHDR8CFxcXW6ZlwtPTA4EB/igsutuouHHLdsyePgXBwUFN3kepVGL4oH5Ys2mHENeo1dDqdCb7KxQKBPj5IioiBFFhQYgKC0ZkWDDcXOV97I5OqVTiiUcnQ6VS4sS5K8K2k2cu4PrNFCyYPRXdu3SyaV5JN29h+aqNKCktM9nWM6E9lsyZ+KP1dmrbiopL8fWqTWho0Apxvl84luKSsv/P3l2HR3WlfwD/jsWVuENwSwhOgADB3d1KoaXusrvd/ra7W9dtaakXd3eKa3AJrglxd8/Y7w9K4MwkkGRmMhn4fp5nnva+V857ZyY34Z77noMla7ehtKxciIe2boZBfcIhqeXD13GJKVi6frsQe2HmBLg4O9Y6VyIiIiIiIiJzYnEjEZGZXL8dj8IHZkMEgPwccVTPlk0bIcjfty7TqlRwoL9Q3Hj7TiL+WLkJYwZHwq2BS6X7dGjVGOt3HUdxaVlFrHFDfzQLDoJSpYZSqYRWq4VCoYCVQg57O1sE+nnD39cL1uzDMRqJRILxQyNha22NHQeOCetS0tLxxbxf0b9PDwzp37vOZnE8d/EKVq7bUmlhWuMgPzw/bSRsbazrJBeyTPsPH0fUSbFgVy6Xw9uLhdCWJOZOAtZt+VNvhNkBvbsjLKTq2YEfJT0jC78tXSPEnp4yFl6e7rU+JhERERERERERERHVzukLV4RlrVaL6zduCLHBA/rWZUpVatyooVDcmJ6RiV8XLkG/Pr3QpVOHSotQunYKw6Yde4UB98YM64ceXTpAqVJBpVJBJpVCrpBDLpPdP4aqTO9YVHsymRTTxwyGrY213qCvefkF+GnBcnTuEIoxwwbCydHBpLkUFRdj8469lc7WCABdw1physh+kMk4qwZVLis7F4tWb0aRzvMkjRsGoGFg3Q9cTLWTX1CIJeu26X2OzRsHYcSAXrUubASAz+cvhkqlrli2tbHGa89MrvXxiIiIiIiIiMyNxY1ERGZyIzZBL5acnCQsD4rsXlfpPFSTRgGQSiXQaO4XoCSlpOOnJWsxqE842rdtqXfj1UohR7d2zbH3+IWKWG5+AYYP6PXI9rTszDMqiUSC4f17wMHeFut3HoBafX90XI1Ggz/3HkL0xavo37s7OoS1hZVCYfQctFotbtyOxf7Dx3Hh8rVKt2nXqglmjh0EKyvjt0+Pj4NHT+KQTmewRCLBqCH9TN4ZTcaTmJyKlRu2CdcjAOjZrSPCO7c36Njf/LQIpQ8U1svlcrzz8myDjklERERERERERERENafVapGcminEVKpyZGfnCLFB/SPrMq0qdQgLxdnoC8JMaSqVGjt378ONm7cxavgQODmJs2LZ2doitHULnD5/sSJ2/dYd9ArvBJnMCrC2qrP8n3RS6d1BXwN8vbB22z6UlIp9zifPROPM+UsIC2mFiPDOaNww0KDiIl134pNwKOokzpy/WOnMnVYKOUYN7ImIziFGbZceLzm5+Vi0erPeQMGBfj6YOHIQvzsWorxciSVrtyE3r0CINwzwxfjh/Q2atfVWbAL+WLlJiM2ZOhqe7g1qfUwiIiIiIiIic2NxIxGRmdyKSxSWS0pKcPXGbSE2uE/9KG50dLDH4Mge2LHviFDgqFSqsGXXIdy4HYcRA3vD3s5W2K9N00ChuDE7Jx8qlRpyOadmNIc+4R3QNDgAS9buQEJKurAuNT0DS1ZvxLotf6Jb5zD07NYJnu5uBrdZUlKK42fO43DUKaSmZ1S6jZ2tNcYP6YNOoS3YGUMPdeT4aew/clyI3StsbNOymZmyopoqLy/HinVbhYdDAKBjuzaI7NnVoGPvORiFeb8tFWLTxo9AYD2YBZmIiIiIiIiIiIjoSZOZnatX5JWVIfYXNW0SjMbBjeoyrSp5eLhjzsxpWLdpK9J0+rVi7sThx18XYPiQgWjVsrmwrmGgn1DcGJeYXCf5kj6JRIJu7dugReMgLN+4C5dvxAjr1Wo1Tp+7iNPnLsLXxwsR3TojrG1LONZyAM3ComJcvHwNB6NOIv4hn3vTRv6YNqo/3Bs416odejLk5uVj0epNyC8oFOL+vl6YOnYoBwm2IOcuXUNGlljI7+vtgcmjBkEulwPQVr7jIxw7HY2RT72OsrLyiphCIcfbz003JF0iIiIiIiIis2NxIxGRGWi1WiSliB1iudmZ0Grv38C0s7VBzy6GzV5lTJ3atYaftwfWbduLrJw8Yd3123H4cdEajBkSieAg/4p4gLe7sJ1Wq0VyWgYC/bzrJGfS5+/tiXeen4Y/D57AjgPH9GZNKy4pwd6DUdh7MAotmgajaeOGCPTzRaC/b7U69crLy5GQnIr4xGTExSch+tJVlJWXV7l92+bBmDyyL5w54x49wqmzF7DnYJRefPjASIS2aWmGjKi2YuMSUVRcIsTatGyKIf173S1w1tauM++PZevw3Fv/guqBB2UkEgnefWWOQfkSERERERERERERUe3EJ6XqxW7cvCksD+7ft67SqRZPTw88M2s69h04jKgTp4R1JaWlWL1+E/pH9kL3bl0q4roD7OXmFUCpVEGh4CM55uLq7IgXZozB8XOXK53FEQCSU9Kwcv0WrFy/BS7OTgj0v9snGuTvC28vT1hbK6CQ3y0mU6pUKFcqkZqWgfjEZCQkJiMuMRnZObkPzcPKSoFRA3qgZ6cQSKUc5JWqptVqsX7bXr2Z/ny9PTFt7DBYcxZYi6JWq/ViYwZHGvQ5rt26BzNeeR+lOtezmROGw9/Xq9bHJSIiIiIiIqoPeCeViMgMcvMKUKYzY1VKsjiSY2T3zvXuBrWvtyfmTh+H3YeO49T5y8K6wqJiLN+wAy/NmgQXZ0cAgK2NFTwbOCM9+34x5J2EZBY3mplMJsOQyHCEtGpS6SyO91y7GYNrN++PZnqvU8/VxRkKuRwKhRxqtRrlShWKi0uQkJSC1PQMoUi3Kna21hg/tA86hXC2Rnq00tIy7DmkX9g4tH9vtA9tbYaMyBBuDVz0Yv6+3pBKpbU6nkajwfuffItP/vez3rpZU8ageZP6MeI3EREREREREdFdtRvYyTQMuDdbywGqjMKgW8q8H01kMpX098QmpgjLZWVluHrthhAbPKB+FTcCgFwux4B+fdC0STA2bN6O/AKx2OhI1AmEd+1c0cfl7+cDiURS0Ud2d6DbNDQM8HtIKwZejwzZ3eBruAH7G9R0zXaWSIBu7VujZeNAbNlzFKcvXNObSfSe3Lx85Obl48Lla4YkWEEmkyKsdTMM79vNzLM1mvN7ZkjDhn5HzfT7vprNlpcrUVBUAqVKBaVKDa0WKCgowLXbcbBSyCGXywAA3p7umD5+OGxsbUyYtBGY8+9CgxjwPXnEri2aBmPPkZPQaO4PNh199Sb69vyrML4Gb5lWq8WXPy7Gux/+T29d25ZN8Nl7rwKSavazaiq/BtYgGcP2N5va9UPfpXn0Jg9jsc/DGPKeEdVz1b1mEmkN/B1QW+b8jhpyzpXta6738EkkkfL6BvA9ILJw9aS4UYvHpiPPnAy5gWCx/5CmJ4n5CqAMa7eytON1ism0Wi2u3RBHKR0U2d2gdk3FykqBof16ommjQGz684Aw+5ZKpUZCcmpFcSMABPi4C8WNN2PiENElrE5ztgRaM/xDzs/LHe88PxWXrsfg0MloXL1556Hb3+vUM4SXuyt6dg5Fl3YtYWdwJ4w5O7Xq098tdcl0561Wq5GSno2ElHTkFRRBpVJDqVJBqwWysrJx+UYsbKys4ORoB0d7Owzu1wud2oeYLB+BuTppHtO/z9zdGqBJo0Dcio2viB09cRYd27WBXF6zf56Ulpbh6Vf+jpUbtuutGz9iIOZ9/F7NPj/eVCMislyG/r426PeuhT5s9cSqb/+OeFL/bUFERERERE+C23cSheXExESh0MPGxga9eobXdVrV1qhhEJ5/5ils3bELl69er4iXlJZCq9VW9F/bWFvD08MNaemZFdvEJaQ8oriR6oqLsyOmjx2EUYMicPzsZRw+GY3M7FyTtOXq7IgenUIQ3qE1nBzs/4ry3/5PsvJyJRJTMxGfkoH45Luv1MwcvQGDc3NzcfOvmW3tba3h1sAZHdq3R0JqFgJ83GFrY22O9KmWGrg6o22Lpoi+cv93x8lzlxDeMRS2NXhOQqVS4eV/foafF6/VWzewdzes+vkzODk6GCVnIiIiIiIiInOqJ8WNRERPlsRksbgxIyMDefniiJ+D+tTfjjwAaNY4CC88NQE/L1mL/IKiiviDHZIAEOjjjjOXb1csx+mM0ErmJZVKEdKyCUJaNkF6Vg6OnIzGsbOXUVxSasQ2JAhp0RgRXULRrFEAZ2okAEB2bj6u3U5AfHIa4pPTkJyaCaVKXem2GRkZuHMnoWLZwcEe9i4eiE3OQpCfF5o3DoSbi1NdpU5G0Kt7Z6G4saCwCOcuXKlRwWpWdg5Gz3gJR06c0Vv3zstz8PF7r9V6NkgiIiIiIiIiIiIiMlyOzqCZaWmpwnLvnuGwtbWty5RqzNbWFn0iegjFjZUJ8PMRihuT09IfsjWZg4OdLfr16IjI8A64djsOh0+ex7VbcShXGjaTmUIuQ9NGAejZKQRtmjdi3wRBrVYj+losDp+6jJtxyXqFjJUpLy+v+P+ikjIgtwg7D5/FzsNnAQBBvp7o0bE1OrZpAisrhclyJ+Pp2SUMF67eqPj8y8rLceLcRfQO71St/QsKizDpuXexY99RvXVzpo7GDx//DQoFvwtERERERET0eGBxIxGRGRQWlQjL8fHxwnKz4CAEB/nXZUq1Ym9nCztbG6G4UbdwzcvNRVguKS2ri9SoFjzdXDFmcG8M69cdF67eRmxCMuKT0pCYkl6jTj2JRAJvjwYI9PVEoJ832rVqAhcnjhZIgEajxdVbd3Do5AVcvhFb7QmWysrE64ZcrkBcUhriktJw5NTdiZZaNW2EiM4haNW0ITuNLUCAnw8aBfkjNu7+qN1HTpxF+9DWkFXj87sVE4ehk5/FzZg4IS6TyfDDZ+/j2RkTjJ4zEREREREREREREdWM6oFBDbVaLWJiYoT1gwf0reuUaqWywiTdPlE7nSLNsrJyUP0klUrQqmlDtGraEBqNBqkZ2Uj4azDOR/WNKuQy+Hl7INDPC4G+Xgj09YS3hxtkMvZNEZCbX4gjZ64g6swV5BUW12hf3f5Qa2txpsa45HTEbU7H+l1R6NauBXp2ag1PnWcxqH5xd3NF6+aNcenarYrY8TMX0a1DKKwfUaCalJKOYdNfRvSVG3rrPvnHK3jnxZkcVJqIiIiIiIgeKyxuJCIyg7JysTNLt7hxUGT3ukzHpBQK8VeNWl35zGxUf1gpFOgY0gIdQ1oAgNCpl5SWidLScihVKihVKshlMijkMigUcni6uSDQ1wt+3h6PvBlPT5bCohIcO3cZR05eRGZOXo33L9e5ZlpZWQnLWi1w+UYsLt+IhZuLE3p0CkG3Dq3haG9nUN5kWr3COwnFjXn5BYi+dA3tQ1o9dL+jJ85i1IwXkJWdK8Qd7O2w+vdvMCiypynSJSIiIiIiIiIiIqIaUj3QL5iVlYWCgkJh/aD+llHcWB26faLlSqWZMqGakEql8PVyh6+XO7qEtQZwd7DO0rIyKFVqqFQqaLWAQi6FQi6DjbUVB9kkPZnZedi45xiir8ZAU93RXf8ilUohkUge2R96T0lpGfYdj8a+49Fo2TgAI/t1Q4CPe61zJ9Pq2aW9UNxYWlaGk+cuoWeXsCr3ib58HcNmvIKkFHEGYGtrKyz69r+YMKK/yfIlIiIiIiIiMhcWNxIRmUHZA51ZpaWlSE8Xb0oOtqDixspGKn2QQi4TllUsbrQ4D3bqVUqrqduEyGKo1RrsOXIaOw6ehLKas3+6NXCFt6c7rK2soFDcLZItKSqAVKJFYVExSkpK9UYqfVBWbj427T6CbfuPYXCvLujfsyNkMlmV25P5BAX4ISjAF3EJyRWxw8dPI7R18yo/s1UbtuOpl/+mN+K1n48Xti7/GaGtm5k0ZyIiIiIiIiIiIiKqgQe6EXUHew1u1BBNmwTXcUKmI9MpeOOAr5ZLKpXAztZGDLI/lCqh0Whx6NRFbNpzrMrZPoG7M716e3ogKMAXAf6+CPT3ha+XJ6ysFBV9Yr8uWIK4+AQUFhUjL78AjRo1hMLKBsmp6VVeT67eTsD12CQM7Nkeg3p2gFzOPtH6xsvDDS2bBuPqzfszFx87E40uYW1gVcmA0Tv2HcHEue+isEic+dPN1QWbFn6D8E7tIPxyJSIiIiICAInk7utJx/eAyKKxuJGIyAwe7NxKTEwUCgRtbWzQq2sHc6RlFBKdPw51ax8l4B+PRE+C5LQsLN2wC3FJaVVuY2NjjZbNmiDQ3weB/r4I8POFQyWzLeZkpSPX3wfA3ZGOu3XpDGtrG8QnJuPqjdsoKS3V20elUmPL3iicv3oL00cPhJ83RyytbyQSCSK6dcKShE0VsZzcfFy8cgPt2rYUttVqtfjsu1/xjw+/1jtOuzYtsWX5T/Dz8eLDBURERERERERERET1iEwmBf4a81W3uHHwgEi9fsX6qrLBXnVzV6nE4qN7AzgS0eMpMzsPSzftw8245Cq3adwwEBHhnRHSugWsrSufifGe/PwCyOVyuDg7wcXZCRNHD0fzZk2gVKlw9fotHIo6iSvXb+ntp9FosOPgaVy4Fovpo/pyFsd6KKJre6G4sbikFKejL/9VqHjfT4vX4OX3PtMrZm0aHIhtS+ahSaPAukiXiIiIiIiIyCxY3EhEZAZWD3Rm6Xbk9Q7vABubqmclszRKlThCoUwmrWJLInoc3Jutcfv+E1XO1Orv642I8M7oFBbyyI48jUaD/ILCimUrhQLtQ9vAz/dusWNZWTlOn7+IQ1EnkZCUord/QnI6PvtpGYb07spZHOuh4IYB8Pf1RmJyakXs8PHTCGndHNK/BgJQKpV44Z3/4Pela/T2H9KvF1b8+hUcHRzqLGciIiIiIiIiIiIiqh75X/fky8rKkJqaKqwbPKCvOVIyGd0+UetKZuQiIsun1Wpx6NQlbNwdVelsjdZWVujUPgQR4Z3h7+tdrWOq1WoUFhUJMWcnRwCAQi5HSOsWCGndAhmZ2Th87BSOnTqLouISYfuktCx8/utaDOzZHoMjOrBPtB7x8fJAs+Ag3IiJq4hFnb6ATu1aQ6FQQKPR4G8ffYcvf1ykt2/PLmFY//vXcGvgUocZExEREREREdU9FjcSEZnBvWIerVarV9w4qE93c6RUa5UMVCooL9ctbuRNdKLHVXZuPn5bua3S2RolEgk6hrVFr/DOaBQUUO3RmAsLi6DRiLPx3evMA+5eT7t36YDwzu1xJz4RB6NO4tTZC8Ioymq1Blv2RiH66i3MmTQMbq7OtTxDMra7szd2xPJ1WytiWTm5uHztJtq2ao78gkJMePpV7DpwVG/f52ZNxncfvwe5nP+kISIiIiIiIiIiIqqPbGysUVBUjMTEROG+vZWVFXr3tJw+Ud2ZGyvr48jOyRWW7e3tTJkSEZmBRqPBiq0HEXX2SqXre3brhJFD+sHO1rZGx83PL9C7zri46Pdnerg3wJjhAzFsUCR27TuMnXsPCf2o92ZxTEjOwOwJA2GlYB9afRHRtYNQ3FhYVIyzF68hpGVTzHz1fazdukdvnymjB+P3rz945GDBRERERERERI8D3sUgIjIDL3dXAEBmZiZKSsQR9QZHWk5HXmV0O/OSM7KFZWdHzq5F9DhKy8jGvEUbkJNXoLfOy8MdMyaNRqOggBofNzcvT1iWy2Wwt7fX204ikaBRUAAaBQWgd/cuWLxyA1LTM4Rt4pPT8fVvq/HyzDHw9nSrcS5kGk0bN4SPlwdS0u5/XoePn4GzkyOGT30OF6/cELaXSCT4/IO38cbzs6pdJEtEREREREREREREdS/QzxsZWTl6g7326hFe6b1+S6XVapGQlCLEAqs5YxsRWQaVSo1FG/bg7OVbeusauLpg+oRRaN40uFbH1u0PtbaygrW1dZXbWykUGDYwEqFtWmLxyvVIShEHnr10Mw7zl27F3MlDYGtjnsI4lVqN9Kw8lJWXQ6lSQ6VSQyaTQiGXQ6GQw6OBM2yeoBlu/X290DgoALfjEipifx44itf+73OcOHtJb/v3XpuD/7z9AvtCiYiIiIiI6InB4kYiIjMI8rvbmaXbkde4YQCaNAo0R0q1pjuCoK6ElExhuXFDf1OmQ0RmkJiSgXmL1qOwSCzWlkgk6NsrHMMGRsJKUbvOqby8fGHZ2cnpkZ04DQP98ffXn8O23Qewe/8R4TqVm1+Ir39fjZdmjkGgr1etciLjkkgkiAjvhFUbtlfEoi9dwwefz0NmVo6wrY2NNZbM/xxjhw+s6zSJiIiIiIiIiIiIqIaaBQfidPQVJCQkCPHBA/qaKSPTyMsvQH5BoRAL9PcxUzZEZGxqtQYL1u3C+asxeut6duuE0UMHwMam6mLER9HrD3V+dH8oAAT4+eDdV+di595DerM43oxLxvxlW/HS9OGwNnERoUqtRnJ6NhKSMxCXnIGElAwkp2dBpdZUuY8EgKe7CwJ9PRHo445AHw/4+3g81gWPvbp1qChuzMrOxbIN25GrM3CwXC7HT5+9h6cnjzJDhkRERERERETmw+JGIiIz8PP2AKBf3DioT7g50jGp+BRx5rRGgX5myoSITOHujI36hY2e7m6YOXlMrWZrfFBevn5xY3UoFAqMGtIf7dq0xKIV65GWcb/Quqi4FN8vWo83Zk/gDI71RIumwfDycENaRhZu3L6DdZv/hFKlErbxcG+ATUvmo2vHduZJkoiIiIiIiIiIiIhqJMjfB9nZ2SgqKhLig/pHmimj2tEd7FW36CguIUlYVsjlcG/QwOR5EZHpaTRaLN+yX6+wUS6TYda08Qhr28rgNnT7Q12cnau9r1wux7CBkWjZrDHm/74MJaWlFetiElLx66qdmDt5CBRymcF56kpIycTh05dw6sINlCtVj97hAVoAaZm5SMvMxakLNwAAUokEbZoFIaJTGzQP9of0MZu1MNDfBw0DfHH89AUsXrMFpWXlwnonRwes/fUL9IvoaqYMiYiIiMhiSaR3X086vgdEFo0/wUREZqBQyGFnY4W0tDQhPjiyu5kyMp4HO/MKikqQky92Vgb+NWslEVm+nLwCzFu0Qa+wMSjAD2+9/IzBhY0AkFvJSKU10TDQH2+9PAeNgsRZY4uKSzFv0Xpk5eQZnCMZ7t7sjbFxCVi1YbteYWPzJo1wbMcqFjYSERERERERERERWRBfLw+9WRsbNHBFi+ZNzZSRacQnJgvLPl7ukEofr6IcoifVjkOncPz8NSFmbWWFF2ZPM0phI1BZf6hjjY/RuFEQXnt+Fhwd7IX41dsJWLn1oEH5PUipUuPkhRv46vf1+PTn1Th65kqNCxurotFqceH6HXy/dCv++/0K7DsWjeKSMqMcu77oGNoaa7bs1itsDPTzxtHNC1nYSERERERERE8sztxYr2gfvclDGXBzXGto24YwZ9u1pzs6I9V3Bvx8GDoaXBVflcL8XOF7JJPJ0M5IN7/r0sN+FuJTMoVlmUwKb0+PR46OoTvaqbHyqeYBar+vpfZR8ncAVdcD3xW1WoNfV2xFTl6BsEnT4IZ4/umpsLGxNkqTeXm6I5XWrLgRAOzt7PDyszPx0x/LceN2bEU8N78Qv67cirefnQSZzPijlRrMkv/WqEXuPp7u2PLnPr3reK/wTli38Ds0cHXBI68ZWk2N2xX3N+A9N/jzMtPn/ZiNekv11RP6PTPo78on9D17Ypnrdz6/Z3WvsvecnwMREdETRavV/7eCWf/+N+RvUTPmbdb7+Pz7jeo5Q64pJrgeyeUyJCeJsxr6+wdApVJBoVAYvT1zuXn7jrDcuFHgo3cyuA/aUvsQap+34c9oWOh7Zsh3xVK/JwbPtmGc/p745HTsPHhaWC2Xy/H801PRrEmj2rehQ7c/1Nmp5v2hABDg54NX5z6Fb378A0XF9wenPX7+GkJbBCOkxcNyfvh7plZrsO94NPYcPY/C4pKHbvsgaysrKBRyyGQyaDQaKJUqlJWXP/LnOT07D+t2RWHzvpPoFtYCI/p2ga2R+p+NopZfsR8WrEKOzufdMbQVNi/+Dt6e7tVs24w/1wZdjwxt25DrggF9yFrOHVLnLLWPzGwzRZnx/bLUz8pS8f1+chj67JOl/ruHiIieeCxuJCIyE91RSn19fXHjTgp8Pd3MlJHxnb8aIyx7ubtBKuWNP6LHwd6os4hLEmefDQrwM2phIwDk5okzK9a2M8/G2hrPPT0F3/28CHfiEyviCcnp2HX4NAb37mJQnmS41977GLk6xbLTxg/Hr998CGtrKzNlRURERERERERERES1lZ9fgMSkRCHm6+uLM9GX0bVjO/MkVQu6RTgPDtSanJqOW7FxwvqmjYLqJC8iMh2lSo0lG/dC88DPv1QqxZzpE4xa2AgAKalpOHD4KLRaLTp3CIOLs3Otj+Xr44UX50zHtz8vRNkDswOu2HoAjQN9YG9nU+NjJqdnY+nGfYhLTn/odh5uDRAY4ItAP18E+vsiwN8Hdra2etuVK5VISk5FfGIK4hOTEJ+YjJS0DGg0+g/yK1UqHDp1CReux2LqiD5o1aQaxeP11M59R/Hz4jVCrEv7ttiz5hfY2+m/T0RERERERERPEhY3EhGZgVarxbnoS0IsICAAh05fQa9OrQ2audDc7qVeXFKGU5duCevat21phoyIyNhS0rOwbd9xIebp7oYX50w3amGjVqvF0eMnsXX7LshkMvSLjMDMqRNrfTwba2u8OGcavpz3G9Iy7s8su+PAcYS0aAw/72qOhklGt3bzTixds1mI9Y3oioXff8qieCIiIiIiIiIiIiILtefAEajV94tVpFIp/Pz8cCjqpEUVNz7M4aiTwrKNtRVat2hipmyIyFh2HjqN5PRsITa4Xy+EtG5h1Ha0Wi1+/G0h4uLvDo599vwFDOzXB21a1/7ZioaB/pgwaiiWrNpQEcsvLMaanYfx1Jj+1T6OWq3Bnqhz2H7gFFTqymcQcnZyRI+uHdG9Swe4OFdvkForhQKNggLQKCigIlZSWopTZy/g4NGTSEnTL6LMzS/CD0u3IjysJcYMDK9fszhWQ3ZOHma/8S8hZmdrgyXff8TCRiIiIiIiIiIAfFKYiMgMTp29oHdDNjAwEGlZubgZl2KmrGpHd6TSe45fuAGlSl2xLJFI0L1LWF2lRUQmolZrsGTDbqjU4s/3jEmj4WBvZ9S2cnNzsWHzdhQUFiI3Lw9rN2zBslXrDDqmvZ0dZk4eIxSR3z2nP6F+4Jyo7qSmZeD5tz8QYs5Ojvjju49Z2EhERERERERERERkwX5bslJY9vHxgZWVFe7EJyI+MdlMWRlPaWkZTpyJFmI9u3aAQs5xxoksWXxyOnYdPiPE/H29MahvhNHbSk1NqyhsBIDCwiKMmzYbR6KOP2SvR+vasR3atGwmxE5duIEL12Krl1dGDr76fT027z1RaWFj8yaN8MyMifjwvTcwdECfahc2VsXWxgYR4Z3xz7dexBsvPI2O7dpCJpPpbRd17io+nL8SV28nVHKU+uulf3yMlLQMIfblv95Ek0aWOxMlERERERERkTHxaWEiIjP4/rfFwrKjoyNcXFwAAIdOXzZDRsYjkUig0Wpx+PQVId6mRRM4OzqYKSsiMpa9UWcRl5QmxPpGhCO4ofE7XmLvxKO0tFSIvf+fT/DOe/+usrC6OhoG+qN/7+5CLCE5HbsOn671Mal2tFotnnn9n8jKzhXi8z79JwL8fMyTFBEREREREREREREZ7MatGOzYfUCINWlyf0bDQzozHtZnuj0S98ZPPHn2AkrLyoR1Ed061k1SRGQSWq0Wyzfvh+aBvkipVIoZk8ZUWmxnqNi4eL1YXl4++g8fj83bdtb6uBKJBFPGjYCtjY0QX7n1AMqVqofuG5OQii9/X4+4ZP0ZFL083fH2y8/g1edmISyktdHfE4lEgibBDfH0tPH44N1X0aJpsN4292ZxPHbumlHbNpWVG3dg5UbxsxzYOxxzZ4wzU0ZERERE9NiRSPm69yIii8WfYCKiOpaaloGV67cKsZYtW1bMIhZ97Q5y8gvNkZrRXI9NQnp2nhDr17OrmbIhImMpKi7F9gMnhJiXhzuGDYo0SXsxd+IqjX/xv+8xa+7LUCqVtT720AF94OPlIcT+PHgCBUXFtT4m1dzvS9di2+6DQmzssAGYOm64mTIiIiIiIiIiIiIiImOY9/NCYdna2hpNmzatWD519gIyMrPrOCvjKS8vx96DR4VY8yYN4ebqYp6EiMgobt5JRkJqphAb3K8X/H29TdJeTGzl/aGlpaUYPWkmfl+0tNbHdnF2wriRg4VYXmExzl6+VeU+124nYN7izSgpFQu3JRIJ+vfugX+8/jwaBQXUOqeacGvggpefnYkp40bA2tpKWKfVarF00z7sOxZdxd71Q1JKGl78+8dCzNXFCb99/a+KZ4SIiIiIiIiIiMWNRER17ueFy4WCHIVCgVatWlUsa7RarNt1zByp1Yru7GlKlUYv/wauzmjSqG5ucBOR6Rw7dwXKB0bylEgkmD5xFKwUCpO0F3tHf6TSexYtW4XRk2aiuLh2xYgKhQLTJ44WOo2UKjWOnbHs2XMtScydBLzx/idCzMvDHT9++QE784iIiIiIiIiIiIgsWF5ePhauWCvEBg/oC8UD/QlKlQpLVm+ARqOp6/SMYvPOvcjIEosz+0eEmykbIjKWQ6cuCsue7m4Y1DfCZO1VNnPjPRqNBnNeeB2ffPE/vecyqqtrx3Zo2rihENM9x3tuxCbhx+Xb9WZ29PJ0x1svzcHoYQOE63hdkEgk6NG1I/755kuVzuK47s+j2H/8Qp3mVF1arRZz3vgAObn5QvyHT/4BPx8v8yRFREREREREVE+xuJGIqA6VlZXhxwXLhNjkscPh4GAvxM5djcWZy7frMjWjOXLmClIycoRY/4iuLFQhsnAajRaHT4kdQx1C2yC4YaDJ2qxq5sZ7tu3cjX7DxiE7O+eh21WlYaA/OoWFCLEjpy5Y7IMUlkStVuOpl/6GQp2ZMn/933/h7uZqpqyIiIiIiIiIiIiIyBgWLFuDwsKiimWpVIrP/vW23mCJt2LicDDqZF2nV2O6RUXZOXnYf/i4EPP19qy08IaILEdufiGir8UKsV7du0Amk5mszZjYO8Kys7OT3jb/+OAjvPb2e7Xqw5RIJIjs2U2IxSWlIy4pXSeWhp9WbIdKrRbirVs0xd9fe67OZmusyr1ZHPv37qG3bu3OIzhx/poZsnq4nxevwZ8HooTYpFGDMGnU4Cr2ICIiIiIiInpysbiRiKgOrd64HWnpmULszRdnY/TAXvrb7jyKgqKSukrNKPIKihB1Trxp7OneAD06h5kpIyIylmu345CZnSfEIrp3NmmbsTrFjf369IKtra0QO3biFHr0H4aExKRatdFL5xyycvNx5eadWh2Lqu/rHxfiyIkzQmz2tPEYNqCPmTIiIiIiIiIiIiIiImNQq9WY98tCITZ62EA0axKMmeOH6W2/adtuZGRm68XrK7VajfMXrwgFj1KpBHOmjoVUysFeiSzZ0TNXhAJCK4UCXTqGmrRN3Zkb33v7dfz9rVf1tvvux18xddZzKC8vr3EbbVo2g6uLsxB7cPbGvIIizF+2DWXlSmGbDu3aYO5Tk2FlZVXjNk1BIpFg9LABGDmkn966pZv241ZcshmyqtzNmDi89e+vhJiPlwe+//gfZsqIiIiIiIiIqH5jcSMRUR3RarX49ucFQqx3j64Iad0SfcI7wN/bQ1hXWFyKVTuO1GWKtXKv206j0SD6+h1oHujIk0gkmDNljElHMiSiunHopDhro6+PFxqbcNZGQH/mxlnTJ2PP1rVwdXUR4lev3UB45BBcvXajxm00DPRHgJ+PENM9VzKui1eu4/1P/ifEGgX54+v/vmuehIiIiIiIiIiIiIjIaLb9uQ8xd8RinVefexoAENa2OVo2bSSsK1cqsWTVBqh1ZgurTx4sZLx2MwZFxeIAtcMG9Ia3p3tdp0VERqRWq3H0zGUh1ql9COx0Bl41tphYsT80uFEQPv73P/G/zz/U23bl2g0YOmYKCgoKa9SGTCZDj64dhdiZSzdRVFwKrVaLFVsPoLC4VFgf1rYVnpo8FnK5vEZt1YWBkREYMbivENNotViycZ9egaY5qFQqPPXq+yguEd/T37/+Nxq4OlexFxERERGRASQSQCLlS8JBp4gsGYsbiYjqyLGTZ3Hm/CUh9sqzMwHcLQJ8bvoYSKXiZfnc1VgcOXu1znKsKbVajbKycgBaXI1J1LvhPbBPOPx9vcyTHBEZTU5eAS5djxViEd06Q2LCfwyq1WrExScKsUYNAxHetTMO79oCP1+xIDExKRk9+g/DsROnatSORCJBRLg4e+OVm7HIysmrYg8yRFlZOWa88C7KH+hYlEgkWDjvUzg6OJgxMyIiIiIiIiIiIiIyhu90BnsNC2mNHt06VSzPnjwSVgqxWOZWbByWrt4kzJhWn0RfvNvHGxuXgDvxiUKfrq+3Jwb0CjdXakRkJDEJqcgrKBJiun2IxlZeXo7EJHG2weCGQQCAV1+ci+ULfoZCoRDW79l/EJFDRiM9PaNGbYV3bi8MSq1UqXHpxh2cungDF6/fEbZt0awxnpo6rl4PYj0wMgL9enUXYpk5+di897iZMrrvi/mLcOx0tBCbO2M8BkV2r2IPIiIiIiIiImJxIxFRHfn254XCclCAH0YM7lex3MDFCWMG9dLbb+X2Izh7JcbU6dXKyXOXUFxSiptxKYhLvnvz3MrKCgDg4eaKjiGtcPn6bZy7dA2nzl/C8TMXcObCFVy4cgM3Y+JRrDOqKRHVTzdiEoRRiW2srdG5fYhJ20xOSUV5ebkQu9eZ17pVC0Tt244WzZsK67Ozc9B36Fhs27mrRm11bNcWtjY2FctaLXA9JqGWmdPD/PuL7xF9+ZoQe/PFp9GzW8cq9iAiIiIiIiIiIiIiS3HpynXsPXhUiL0yd5YwWKKdrQ1mjh+ut++JM+exZtMOoT+iPkhLS8fps9FISErBleu3AAAuLi4AAKlUgjlTx+oNYEtElicuKV1Y9vXxQoCfTxVbG6nN+AS9a16jv/pDAWDyhDHYunYZ7O3thG1Onz2PHv2HIfaOOOvjwzg7OaJV8yZC7MadJKzZfliIOTrY4+mp46CohzM2PkgikWDU0P4IbhggxA+cuIibd5Kr2Mv0zl+6hg++nC/EGjcMwJf/esNMGRERERERERFZhvp9J8Js6tfN8uqz1Lxrr751bNDDGT7DlwH7m3WqaS0Sk1KwbstOIfrSnOmQyaR48Ge3T3h7nDh3GQkp92+ca7VaLNywDzKpFKEtGtZRzo9WWFSMA8dO43ZCKm7GpQAA7O3tYWd398Z6Xn4BPvzfr488jnsDFwT6+SDQ3weBft4I8veFreKRuz3EE3pdMOv10FLfc0vN2wC1/J7EJaUJy82bBsPGxtoYGVVJtzPOzs4Onp4eFcuBAf44snsrho6dghOnzlTES0pKMHLCDPzx47eYMXVitdqytrZCi2aNce7C5YpYfHIawju0NvAsYNjPpuTxehgi6uRZfD7vNyHWpmVT/OfdV8QNDbqePYE/10Q1Ys6/iYnqOYN+Z1vqz5Y5f2+a8z2z1M+LiIiIqL6x0PswBqdtoedtsf9uMSe+Z7Ux75eFwrKHuxsmjdUvZAxr2xydw9rg5LlLQvzg0RNQqVSYPHZ4vSgY1Gq12LFrL2LjEnD52k0AgFQqha+vLwBgwshB8PZyr+XRn8zv2JP5jIehn7UZ71sZ8nEZ0rTB35OaN56QIs6E2CjQ38AcHi0mVuwPdXV1gYuLsxAb0K8P9u/YiCFjJiMzM6sifvNWDMIjh2DnxlUIDWlTrfYaBvrh4pXrFcsXrsWiuLRM2GbS2OFwsLev6amYhVQqxfSJo/HxV/OhVKkq4ks37cM/np8IayuDHjx5uEp+tkpLyzDj5fegVN7PRSqVYtG8j2Cv+56a61po8N+EBvxu1qoNbNsABvW7189ZpakKFvuMxZP5d6FBLPazJiIiIqKq8C88IqI6MP+PZVCr79+os7OzxezpE/S2k0gkeG3ORDg5iDc21RoNflu7Gycu3DB5rtW15/AJRF+LxfXYpIpYYGBgRRFr+QM3bB8mMzsXZy9excYd+/Ddb8vx7n+/wYLV23A7LukJ7dwiqn8SksWRSgP9fU3epm5nXqOgQL0ieTe3Bti7bR0GD+grxNVqNWY++xK++Ob7arcX6C+OvBqfnFbFllQbhYVFmPni36DR3O/8USgUWPzD5yYvlCUiIiIiIiIiIiIi08vOycWSVeuF2HOzpsLGxqbS7WeOH4rWzYL14kdPnMHvS1ajuKTEJHnWxMXLV/Dn3oMVhY0A4OPjA2trawzt3wsR3TqaMTsiMqZ4nf7QAD/T94fGxsULy42CAivdrlOHMBzdsw1BgeIshalp6YgYOAIHDx+tdD9dgTrnpFvY2LFdW4S1bVWtY9UXXh7uGDFY7CvOzMnHrsNn6zQPjUaDv3/8LS5duyXE33lpFsI7tavTXIiIiIiIiIgsEWduJCIysZKSUvyyaKUQmzlpDFx1Rty7x9bGGu+9PAP//t8fKC65fzNZo9Vi8aYDSEjNxIg+nWGlMN8l/NqtOPy+ajvSsnIrYm5ubnBwcDD42Cq1Gqeir+JU9FX4e3sgoks7dAxtCRtrK4OPTUQ1p9FokJgqjlRaF8WNep15DSvvzLO3t8em1Usw+/nXsGTFamHdO//8N9LSM/D5R/965AjPuucUn5SGjMwslJSWoaysHEEBvrCy4nWoNhKSUvDaex/j9h3xM/3gnZfQrm1LM2VFRERERERERERERMb066IVKCkprViWy+V47ulpVW4vkUjw/Mzx+N+vy3HrToKw7tzFK4iNT8TUcSPQumUzk+X8MHfiE/H3Dz5FekZmRczKygpeXl7o070zhvSLMEteRGR8JaXlSH/g2QcACAqo+8FegxsFVblts6aNEbVvOwaNnIiLl69UxPPzCzBw5EQs+e0HhHftjMLCIhQWFaGwsPCv/95dLigoRFZ2Do4fPw6lUqn3ArQoys9GWJvmCGsXYqpTNok+Pbvh3MUriHngd8nh05cxKKIDFCZ+rkaj0WDd1j348JufcfHqTWFdaOvm+OCtF0zaPhEREREREdHjgsWNREQmtnztZmRl5wixl5+Z8dB9HB3s8d4rs/DRdwuEAkcA2H/iEi7fTMC0Eb3QOMDb6Pk+jFarxelLt/D+l78gOye3Ii6VSuHv71/pPg72drCztYVCIYdUKoVKpUK5UoW8vHyoHpjNsjKJqRlYvmk31u88iP49O2NARCfIZDJjnhIRPUJqZo7eTKzmmLmxYVBAFVvenQFw4S/z4OXpgS+//UFY99V38xGXkIDOHdoLHXgP/regsBD5BQVISk6t6MDTaDSYP//HiuM42NvhmRkT8frzT8Hft26vvZbqTnwiPv3uVyxYvv6vTtH7unVqh7dfmm2mzIiIiIiIiIiIiIjImFQqFX74bbEQmzB6KHx9vB66n1QqwWvPTMGPi1bh8o07wrrcvHz88PtSdOsUhrEjBsHO1tbYaVdKrVZj1/4j+GXBUqGwEQACAgIwtH9vDB/YGxKJBIC2TnIiItNKSBFnbZTJZI+8fhlDbJzYH3r9xm3cjolF4+BGlW7v6+ONQ7s2Y8SEaTh89HhFvKysDBOmzzE4n8XLVmHxslUY0Lc33n3jFfTp1eOva139JpVKMWXcSHz45fcVsaKSUpy9chtdQpubpE21Wo3Vm//ER9/8iis3buutt7JSYPH3H8HKSmGS9omIiIiIBBLJ3deTju8BkUVjcSMRkQlptVp8+/NCITagT0+0bN7kkfu6Ojngg9dn46N5i5BXUCSsS8/OwzcLN6NPl7YY3qdTnczimF9YjJXbj2Dv0dNCYSMA+PrendHMydEBgf6+wsvF2anS46lUKqSkZSA+MbnilZicCnUlBY+lZeXYsucIzl+5iRljB8HP28MUp0hElUhIFjvzXF2c4ehgb/p2k5KE5QVLVqB1yxaYO3tmpZ1oUqkUX3z8Abw8PfD2ex8I69Zu2IK1G7YYlE9hUTG++XEBvv9tKaaOG463X56Dls0aG3TMx1XMnQR8/M1PWLxqI1Qqld56OztbLPr+M8jl/KcIERERERERERER0eNg47Y/kZCYLMRemTurWvtKpRI8P30Mlq7fiePnruitP3bqHK7euI0Rg/uhQ2hrKBSmKRTRaDS4djMGm3fswc3bsUhOFs/H0dERc6ZPQL9e4SZpn4jMJyM7T1j29nSHog76sXT7PC9evoLQrr3x+Yf/wnNznoJUKtXbx8XFGX9uWo0ps57Dxi3bTZLXrr0HsGvvAXRs3w7vvP4SxowcVu8Hofb19kTzpsG4fjOmInb41CWjFzeqVCqs2LADH/3vF9y4HVfldl/835toa6aZh4mIiIiIiIgsEZ8oJiIyoQNHjuPiletC7NXnZlZ7f3s7W/zfa0/j+4VrEZuQIqzTAth34iLOXY1Bzw6tEB7WAo72xh+xND0rD4fPXsGxc9dRWFyCxMREYb2NjQ06d2yPkUP6o0XT4EpvsFdGLpcjwM8HAX4+6N6lAwCgsKgIx06ew6Fjp/RmuwSAhOQ0fDp/CYb06YYBEZ3r/Q10osdBfmGxsOzt6V4n7fp4iaOhFhUV4/lX38b6Tdvw+4//Q4C/X6X7vfXai/Dy9MDTz79aaWGdoZRKJRauWI+FK9ZjxKBIvPPKMwjv3N7o7Viim7fv4ONvfsLSNZsrLVQHABsbayyZ/zmaBAfVcXZEREREREREREREZCrf/rRAWO7SMQxdOoZVe3+JRILpYwcjpGUTLFyzHeVK8f5+bl4+Fq9cj/VbdiK8c3v06NoJ7m6uRsm9qLgYx0/d7Z/MyMwGACQkJECj0VRsY2NjjU8/+Bs6tw8xSptEVL8oda45dnZ1M1Ps7BlTsW7jVuF6U1RUjBdff7eiTzQoMEBvP1tbW6xZ+jteeO0d/LpgicnyO332PCZMn4MmjRvhrVdfxMypE2FjY2Oy9gwVEd5ZKG6MTUxDfHIGAn0NHzxbqVRi6dpt+PjbX3H7TkKV27Vq1hj//dtLGD2kr8FtEhERERERET1JWNxIRGRC3+nM2ti0cUMM6turRsewsbbCW3OnIOr0BazcshdqtUZYn5NfhM37T2HbwTMIaxWMiI6tEOzvVenMZtWl1mhw6WY8Dp++gqsx94sZk5OToVQqK5Yd7O3wwd/fRK+exhmh1MHeHv379EDfXuG4ev0WDkadxOVrN6HVau/nptZgy56jOH/lFmZNGAJvDzejtE1EldPtzLOysqqTdj/97/s4fuoM7sTFC/Hd+w6gTaee+ObT/2LWjCmVXuumT5kAd7cGGDdtNoqLi/XWG8vmnfuweec+9OjaAe+8/AyG9O9V7QLvx8nVG7fx8dc/YsX6bULn64OsrBSYM2083n3lGQT4+dRxhkRERERERERERERkKmfPX8SRY6eE2KvPVW/WRl2hrZri078/j1+Xb8bVW/ozYhUWFWPX/iPYfeAoWrdoivYhrRHo7wsvT/dqD4qq1WqRlZ2L+MRkXLp6A2fOX4TygcES8/PzkZNzfxDWoABfzJw0joWNRI8xpc6AqVYmmiFW18D+kdi7bT1mPfeKXp/o3gOH0LZzBL7+9D+YPXOaXp+oXC7Hz/O+QmCAH/798ZfCoK9WVlZwcLCHg739/f/+9f+JyWkoLSuDQqGoeI0ZPgiFhYWY/8sCZGRm6uV563YsnnvlLfzrw8/w6gvP4vlnZsHFxdk0b4oBQlo1h7OTI/LyCypih09dwtSRfWp9zPJyJRat3oRPvv0NdxKSq9yubcumeP+NuRgztN8T2V9MREREREREZCgWNxIRmUhsXAI2bd8txF5+Zkatb2SGdwxBaKummL94Pe4kpuqtV2s0OH3pFk5fugXPBs5o6OeJQB93BPh4wN/bDTZWVd+ALyopRXxKJhJSMhGXnIHYxDTk6czWVlpairS0NACAVCpF21bN0adnuNEKGx8klUrRumUztG7ZDHfiE7F45QakpmcI2yQkp+Grn1fgxafGoqE/C2WITEW3M0+hqJs/H4MCAxB9/ADe+se/9EYczc8vwOwXXsP6zdvwy/dfw9fHW2//wQP74cjuLfjvZ18jPSMDjg4O+p14Ov89fiYasXGJFR15fcI7YMKwPkjPyMLXPy7AwhXrUVpaptfWkeNncOT4GbRu0RRvvzwHk8cMhaKOOj3N6dLVG/joqx+xetMOoQj9QTY21nh2xkS8/dJs+Pl4VboNEREREREREREREVku3cFefbw9MXbE4Fofz9rKCi89NQ7RV25WOosjcLdA8dLVG7h09QaAu4VI/n7eCPT3ha+3F6ytrKBQyCGRSKBUqqBUKpGWmYX4hCQkJKWgqLik0rY1Gg3i4+8WGFlbW6FDSGv4+XihX58etT4fIqr/dAeYrm6xtDH0juiOCycO4u33PsDPvy8S1hUUFOKZF9/A+k3b8OsP38DPV3wuQiKR4J/vvolXX5iLnNzcin7Phw1W+96HXyEnN69iuU3LZnhh9jQAwLtvvIyFS1fiy2/nIyb2jt6+aekZ+McHH+GTr77F3Kdn4rWX5urlZE4ymQw9u3bE1l37K2LR12IwZUTvGg8OXlZWjj9WbMCn835HQpL+8zn3hLVtgfffeA4jBvZmUSMRERERERGRAVjcSERkIt//ukgo9nB0cMBTk8cadEx7O1u8/dxURJ2+gDXbDqD8gVkUH5SenYf07DycvHgTACAB4OnmAgc7GyjkMkhlUqhUaqhUauQVFiMrt6DS4zwoPj4eWq0Wbg1cEBbSGna2thjUP9Kg86mOhoH++Pvrz2Hb7gPYvf+I8J4WlZTi299X4/npo9EsONDkuRBR3XJycsQv33+NsSOHYfYLryEpOUVYv23nbrTp1BPzvvwEUyaO1euUCmsXgvUrFla7vRKlBmXK+52Xbg1c4OToACdHB8z/4gN88M7L+O7XxZj/+3Lk5uXr7X/52k089eK7eP/j/+H155/CnGnj4WBvV7OTtgDRl67hw6/mY92WP6vcxs7OFs89NQlvvfA0vL086jA7IiIiIiIiIiIiIqoraekZWLFusxB7Yfb0hxbWVFdoq6b4/B8vYvPuIzh29hJKKhl88J5ypRIxdxIQcyfBoDYzMjKgUavRsmkwggL8IJNJ0bd3D9ja2hp0XCKq3+RysZhRWcVzGKbi6OiAn777EmNGDMXsF15DYpI4Q+COXXvRplNPfPfFx5g2ebxen6ijowMcHR0e2Y5Wq0VBYaEQ69w+tOL/bW1t8fwzs/DMrOlYt3ELPvt6Hs5FX9Q7TkFBIb789gd8O/8XTJ88Hm+/9hJaNG9ak1M2mc4dQoXixqKSMmTlFsDd1ala+5eUlOK3Zevx+Q9/ICklvcrtOrVrg/ffmIuh/SNqXDhJRERERERERPo4ZBARkQkUFhbh9yWrhdjTU8dV64ZydYR3DMHX//cypozqDzdX50durwWQlpWL2wmpuBabhCu3EnDjTjJiEtOqVdiYm5sLrUaNnt06oWvHMFhbWaFrpw5wc2tghLN5NIVCgVFD+uPtl5+Bt6dYJFNWrsT8xetxOy6pTnIhetIo5OJYGMpKRkg2tYH9I3Hp1GHMnDpRb11OTi6mzX4eY6fMQlpa1R1M1aF7brrn7unhhg//8Trizu/Hl//5W5UzESYkpeCNf36CoHZ98P4n3yI9I8ugvOqLM+cvYfT0FxDWe2SVhY0O9nZ499VnEXtmL77897ssbCQiIiIiIiIiIiJ6jP2ycDnKy8srlq2trTF31lSjHV+hkGPskN748p8v4dkpI+Dj5W60Yz9IIgGC/H3QyN8bfSO6IrhhAGQyKXy8PREW2tYkbRJR/aHXH6qq+/5QABjQrw8unjyEWdMn663Lzc3DjGdexOhJM5Gamlar42fn5EGlUguxhoF+etvJ5XJMHDcaZ47uxa7Na9CvT69Kj6dUKvHH4uVo1aE7Rk+aieMnT9cqL2Nya+AKezuxID0+OeOR+xUXl+Cbn5egcZchePWfn1ZZ2Ni1Qwi2L5+P4zuWYdiAXixsJCIiIqL6QSLl696LiCwWf4KJiExg8cr1yMu/XzQokUjw0jPTjdqGRCJB944h+M+bc/D+q7PQqmlDSKXGv3Gq1Wrh49EAPbt1gtNfxZkO9vaI6NHN6G09SsNAf7z76ly0bNZYiJcrVZi/eD0SHzJyHhHVjkIhduY9+JBCXXJxccbCX77HptVL4O3lqbd+w+ZtaN2pJ9as31TrNnTPTaEzSus9jo4OeOOFWbh9Zg/+mPeJ3jXpnpzcPHz09Y9o2D4SL77zH4NHjAbujhZ66eoNJKWkGnys6jp59gKGT56LTv3GYtOOvZVu4+TogPfefB6x5/bhk/ffhId73RS/ExEREREREREREZF5lJeXY/7vS4TY5LEj4OHuZpL2Qls1xT9fnomP3pmLLu1aoYGLE2Sy2j/yIpPJ4OneAP17h+Pjf76BVk2C4GBvKxSqDO4fCamUj9UQPe5sbcTZZnNy8syUyd0+0T9++g5b1i6rtE9009YdaN2pJ1at3VDjY8frzAhpZ2sLtwauVW4vkUjQv29v7N66FqeP7MGEsSMrvSZqtVps3LId3foMRq+BI7B9525otdoa5ZaSkoqtO3bh3x9/gZETpqNVh+7oNXAE/vvpVzh+8jRU1Sw4lUgkCPD3FWIJD3mOpLCoGF/8sADBnQfjzX99gdT0zEq369m1PXat/gVHty7BoMgeLGokIiIiIiIiMjL5ozchIqKa0Gg0mPfrIiE2dEAfNAluaLI2vT0a4MWZY6FWa3Dp+m1cvBaDuKRUZOXkoaxcWevjyuVydAxtiZiYWCHeP7IXrK2tDU27VqytrfDc01OxYNlanL94pSJeUlqGHxatx3svT9cbiY+Ias/JwU5YrqpDp66MGDoI3bt2xstv/h0r1qwX1mVlZWPC9DmYuGkrvv/qU7jX8AGK1DRx1E4nR/uHbm9lZYWnJo/BjImjsG3XAXw+71ccPXFWb7vS0jL8uGA5fl60EhNGDsLbL81BWEirR+ZTXFyC6MvXcPbCZZyJvoyz0Zdw+dotqNV3R1RtGtwQ/ft0R/9e3dGnZ9eKAnRjiTp5Fv/98gf8ue9Ilds4Ozni1bkz8ercGXB1efRMwkRERERERERERET0eFizcZveffVXn5tl8nZdnBwwY9zgiuWcwjKcv3wd12/dQXJqBkrLyqDRaKDWaAAAMqkUMqkU9vZ2CPTzRqtmjdGkYQAauDrfLU6RypCSmoaz0ReFdtq2boHAAH+Tnw8RmZ+vl9inmJWTi8KiYjjY21Wxh+kNGzwAl08fwStv/QPLVq0V1mVn52DSzGexbtNW/PD1Z/DwqN6stnEJScJyoL9PtYv0OoSFYtXi33Drdgy++u5HLFiyAmVlZXrbHTpyDIeOHEPb1q3wzusvYeK4UVAoFBXrtVotkpJTcPb8BZw5F13xSk3TL0C8+tfx/u+/n8LFxRmRvXqgf2RvDOjbG8GNGlaZa5C/L67duF2xXNnMjfkFhfjhjxX4+qfFyMrOrfJYfbp3wvtvPIfe3TtVuQ0RERERERERGY7FjURERrZ7/2HhRikAvDr3qTppWyaTIrRVU4S2agoAkMisUFZejis3YpGUko7SsjKUlZdDo9HC2soKVlZyuDg7wt3VFb8tX49y5f3R7qRSKaaMHY59Bw4Ibfj7+SCkbes6OZ+qKORyzJ42Hj8vXIFLV29UxPMKCrF2+wHMfKBDk4gME+grjgiak5uHgoJCOBq5kK4m3NwaYPnCnzF21DA89+rbyMzMEtavWrsR+w8exS/ff4WRw6p3PSgsKkZWTq4QC/DRHw21MlKpFMMHRWL4oEgcPXEGn3/3K7b8uV9vO41Gg5UbtmPlhu0Y0Ls73nnlGfTp0QUSiQRFRcU4f+kazkRfwrkLV3DmwmVcvRFTUchYmZsxd3Az5g7m/74MMpkMXTqEon/v7ujfuzs6tw+BXF67P/UPRZ3Cf7/8HnsPHatymwauLnj9+afw0pxpcHZyrFU7RERERERERERPHu1frwdDNZvVRp+FzhpjttluDH2/DWHGz8rg75mZcFakulXD78l3Py8QliPCO6Nd21a1+r5JpLIa73NPA1dXRPboisgeXWu1v1arxY5d+4RZxhQKOfr36VWNnWvV5F2Gfr+1huxv6M+WhV5TDDlvQ94ys/6uN7BtQ76nBv18GLBvLfh4ukEul0Glut8vl5CUjJbNmtRtIjoaNHDF0j9+xNhRwzD35beQkSkOQrtm/WYcOBSFn+d9idEjhj7yeLdj44TlQJ0ZDqujSeNg/PjtF/jgH2/jux9/xfxfFyA3V3+my4uXr2D6nBfw3r8/xvNznkJhUdFfBY0XkJ6hX2z4KLm5eVi/aRvWb9oGAAhu1BD9I3thQN/eiOzVEy4PDMKqe17xyRnQarWQSCTIzcvHvN+X438/L0FObn6V7fXv1Q3/fP1Z9OzaofINDP25NmR/rcawts3F4N97xkmjxiScwZmqy0L/7WLOf3Px54voMVPZL2tL/bcjERGZQ70obtRqtcKNYnOr7qhUZBz16bOnes6gn01DbrrX7Mbgtz+JHXmtmjdF317htWpaYug/4iVSWFvbIKxtS4S1bVnpJhqNFt/9tkwobASAmZPGIDExAeU6Mz8OHtCvXlwnZTIZ5syYiO9/WYxbD9yEP3n+Ktq3aY62LRrX7sBm7ZgyF0vNm+qCl7srrKwUwrUgPjEZrVs2M2NWd40dNRw9w7vihdffwbqNW4V16RkZGDVxBqZPnoBvv/gIrq4uDz1WfGKysKyQy+Dj2aDGOXXv0gGblnXA5Ws38eX3v2PZ2i1QqVR62+06cBS7DhxFSOvmUKnUuHYzBhpN7Tui1Go1ok6eRdTJs/j35/Pg5OiAyJ5d0a9XOPr3CkeT4KCHXru1Wi32HzmB/375Aw5GnapyO3c3V7z5wiy88PQUODr8NbNlZb8nzdUhZ7HXYaLqMv/fYERUCUN//9SDf1/VPQv+nW1Q6uzUIyIiIiKix8PxU2dx8ky0EHtlrulnbTSFS1euIT5RnM0sIrwrnDiwH9ETQyaTwc/LHXFJaRWx+ETzFzfeM3rEUPTo1gUvvP4O1m7YIqzLyMzEmMlPYerEcfjuy4/RoIFrlcfJyMwWlv18vWudk5eXJz764D387c1X8csfi/HN9z8hKTlFb7v4hET8/V8f1rqdqsTE3sHPv9/Bz78vglQqReeO7dE/shf6R/ZCcHAjYdvi0jIkp2Xgl8Vr8N2vy5CXX1DlcQdH9sA/35iLbh1DjZ4zEREREREREVWNQ18QERnRjVsx2LHnoBB7Ze6MelEMWJUjJ8/iRow4Ql/3Lh3g7emG6IuXhXhYaFv4+frUZXoPZaVQ4Olp42FrYyPEl2/cjaLiEjNlRfR4kUql8Pd2F2LxSclVbF33PD09sGbpH1i+4OdKCxiXrFiNNp16Ysefex56HN3iRj9vD8hktR8punWLpljw/ae4fXo3XntuJuzt7Crd7sLl67hy/ZZBhY2VyS8oxMbte/DSu/9B866D0LhjPzz7xvtYs2knsh+YoVKr1WLX/iOIGDYV/cY8VWVho6eHG7749zuIPbMH777yzP3CRiIiIiIiIiIiIiJ64nz380JhOdDfDyOH9DdPMgYoLy/Hrr1i366rizO6delopoyIyFwCfT2F5fhE/UI9c/LwcMeapX9g5aJfKi1gXLZqLdp06oltO3dVeYyy8nJh2U7nOYvacHR0wJuvvoCYy6fxx4/fokXzpgYdK6JHN7z+0nP4bf43eO3FuWjdssUj99NoNDh+8jT+++lXiBgwAi3bdcP27dtx8eJFZGRk4MSJE2gVPhz//eqnKgsbRwzsjRM7lmPb8vksbCQiIiIiCyS9OyPuk/5iaRSRRasXMzcSET0u5v26WFh2dXHG9AmjzZTNo2Vm52LD9r1CzNXFGaOHDcDSFWuEuLWVFfr27lmX6VWLi7MTxo0YhCWrN1bE8guLsHb7AcwcN9h8iRE9RgJ9vRATf78Dr7515kkkEkyeMAa9e4Zj7itvYcv2P4X1ySmpGDJmMmbPnIqvP/1vpaMtJ+gUN+p2YNZWgJ8Pvv7v3/HPN57H/D9WYN5vS/RGRX0YqVSKFk2D0SG0NcLatkKH0NYIad0ct2PjsevAUew5GIUjJ87ozbKr6058En5bsga/LVkDiUSCju3aoHePLjgUdQondEbXfpCPlwfeeXkOnpk+HnZ2ttXOm4iIiIiIiIiIiIgeT0nJqVizabsQe+mZGZDLLe/xk8NRJ1BQWCjEBvbtbZHnQkSGCfTzBB4YA/R2bBzUarVBg6GawsRxo9Grx90+0c3bdgrrUlLTMGzsVMyaPhnffPYhnJ2dhPVKpUpYVigURsvLysoKs2ZMwcxpk7B1xy589vV3iDpe+aCqAODs7IT2oSFo3y4EHcJC0SEsFE0aN4JUqv8wclJyCvbsO4hdew9gz/5DSM/IeGguBYWFKCgsRFxc3EO3A4AxQ/vivdeeRVjblo8+SSIiIiIiIiIyGd6RJSIykrz8fCxcsU6IPTNjYr0uBlm/bQ/KdApipo4fievXbyI5JVWI947oDgcHh7pMr9q6dgrD2QuXcfnazYrYyfNX0L1jWzRp6G/GzIgeD7qFftdu3kZpaRlsbKzNlFHlfHy8sWn1EixetgqvvvMe8vLyhfW/L1qGXXsP4I8fv0W/yF4V8dKyMly9eVvYNsDXy6i5NXB1wT/ffB5vvjALC1duwFfz/0DMnQRhG6lUilbNG6NDSGu0D22NDqGtEdq6Bezt9Wd9bB96d5u/vfosiotLcOjYaew+eLfY8eKVGw/NRavV4tS5izh17mKV2/j7euPdV+Zg9tRx9e5zJiIiIiIiIiIiIiLz+fGPpVCp7hfI2NraYPb0iWbMqHays3MQdeK0EGvcKAjNmzUxU0ZEZE7BAT7Ccn5BIS5cuY6wtq3MlFHVvL29sHHVYixdsQYvv/V3vT7RBUtWYPe+g/h9/v8woF8fAEBcQhI0Go2wnVQqMXpuUqkUI4YOwoihg3Ak6ji++/FXXLl6A95enugQFor27dqiQ1goghs1rLSQsTJ+vj6YOW0SZk6bBI1Gg4uXrmDX3gPYve8gDkcdR2lpaY1ylEgkGD98AN57/Rm0bdmsNqdJREREREREREbG4kYiIiNZsGwtCguLKpalUilemD3djBk9XFZOHqJ1CmC6d+mA4CB/zPvxNyHu7tYAnTu2r8v0akQikWDKuBH48MsfUPLAjeuDx8+xuJHICJoHB0AikUCr1QIAysrKcfJsNCLCO5s5M30SiQQzp01C3z4RmPPCa/hzz35hfUJiEvoPH4fnn5mFzz/8Pzg4OOD0uYsoLS174BhAi8YBJsnP1tYGz8+ajGemj8fWXQdw4cp1uLm63J2RsVXzWhXE29nZYlDfnhjU9+7suimp6dhz6Bj2HIzC7gNRSE1/+OilDwr098XfX3sWT00aDWtrqxrnQkRERERERERERESPr9LSUvy8cLkQmzFxDBq4upgnIQP8ufcA1Gr13U4B3O3bHdSvDyQS4xf7EFH95+3RAI38vRGbeH8Q6ENRJ+tlcSNwt090+pQJiOzdE3NeeA07d+8T1icmJWPgyAmYO3smvvjoAxw6pj+LokqlNmmOPcK7okd4V6MeUyqVIjSkDUJD2uDt119CSUkJjkSdwO59B7Fr735EX7z80H0njRqEf7z2LFo1bwz81fdNREREREREROZXvSGQiIjoodRqNeb9uliIjR46AEEBfmbK6NGOnDxbUagEADbW1hgzbCAOHo5CUXGxsO2g/pGQyWR1nWKNuLo4Y3C/XkLs/JVbyCsoNFNGRI8PFycHhLQIFmKHok4K15D6xt/PFzs2rsIv338NBwd7vfU//roAoV374ODhKBw8ekJY17pZIzRwcTJpfnK5HKOG9MP/vfUiXpw9FV07tjPaTL8+3p6YPmEkFv3wGZIuHUL0wU348j/vYlBkT9ja2lS6T6Mgf/z6zYe4cWIn5s6cxMJGIiIiIiIiIiIiItKzYt1mZGZlC7FX5s4yUza1d+t2LK7fvC3EunQMg4eHu5kyIqL6IKJLiLB8/WZMjQYRNQc/Xx9s37ASv83/Bo6ODnrrf/59Edp2jsCGzdv11pUrlXWRoknZ2tqif9/e+Pyjf+H88QNIjbmMX3/4Bs2bN4e9/d0+YolEgsljhuDy4Y1Y+uNndwsbiYiIiIiIiKheYXEjEZERbNu1HzF34oXYq889ZZ5kqkGpUiHqVLQQ69IhFIWFhThx+qwQb960CZo0Foua6qvwzu2hUCgqljUaDaJOXzRjRkSPj56d2grLyanpuBUTZ6ZsqkcikeCZWdNx8eQh9Inoobc+JvYO+gwehVVr1kH5QOddROcQvW0tlUQiQdtWzfHG87OwfdWvyLpxAnvWL8S7rzyDrh1DEd4pDAvmfYJrx3Zg9rRxsLJiUSMRERERERERERER6dNqtfj2pwVCrF/vHmjVoqmZMqodtVqNnXv2CzF7Ozv06tHNTBkRUX0R1qoJHOzEgUIPVzLjYX0jkUgwe+Y0XDx5CH17R+itj4tPwIYNG3DkyBGhTzQrO7cOs6wbXl6e6NunFyIjIzF9+nRMnToVs556CgvnfYzmTRqZOz0iIiIiIiIiqgKLG4mIjOC7XxYKy+3atkKPrp3Mk0w1nL90HQWFRUKsR7dO2Ll7HzQaTUVMJpNhYL8+dZ1erdnZ2aJTmFiAdeTUBajVmir2IKLqah4cCE83FyF2MOqkeZKpoYZBgdizbR2+//pT2NnZCeu0Wi0uXryINWvWIDU1FW6uTmjZpKF5Eq0DNjbWiOzZFZ+8/yaidqzCke0rMHPSaKEwnIiIiIiIiIiIiIhI1+Gok4i+dFWIvWqBszaePH1Ob/bJfn16wsbGpoo9iOhJoVDI0a19KyF27OQ55OblmymjmgkKDMCuLWvwwzef6fWJAqjoE01JSQEAxCcm13WKdSIu4e55SSQSODk5ISjABwq5zMxZERERERGZkETC170XEVksFjcSERno0pXr2HswSoi9OvcpSOrxH0mHjp8RlpsGN0RBfj5ux94R4uFdOqFBA9c6zMxwEeGdheXc/EJcvH7bTNkQPT6kUone7I3nLlzGrdj6PXvjPVKpFC/OnY3o4/vRI7yL3vq8vDxs3LgR169eRnl5uRkyJCIiIiIiIiIiIiKqv779WZy1sXGjIAwZYDmDpAKASqXCgSPHhJivtxfahbQxU0ZEVN/06NhGeB62tKwMy9ZsglarNV9SNSCVSvHCs0/j4smDiKhkRtp7faJRUVG4bSH9vDWlW7QZ6ONhpkyIiIiIiIiIqLpY3EhEZKDvflkkLHu4u2HSmOFmyubRiotLcPtOghCLCO+MYydOCTFHBwf07N61LlMzikB/XzQK8hdiF6+xuJHIGLqGtYKVQl6xrNVqsXTVRosqBmzSOBgHdm7C5x99ALlcLqzTarVYs2ErOvYdg9PnLpopQyIiIiIiIiIiIiKi+uVOfAI2btslxF5+diakUst65CQ1LR1lZWVCbPCAyHo9aC0R1S13V2d0CW0pxC5fu4njp8+bJ6FaCm7UED999zXCw8Mhk+nPWhgdHY15P8zH4aPHzZCdacUn6RQ3+rK4kYiIiIiIiKi+s6w7zURE9UxWdg6WrtkoxObOnAwbG2vzJFQN8cmpwrJcLkdImxbIyMwS4n0iusPKyqouUzOaDu3E2eXik9LMlAnR48XO1gZD+ohFz+mZWdi8c6+ZMqodmUyGxk2aYNy4cfD09NRbf+X6LXQbNBHvf/I/iyrcJCIiIiIiIiIiIiIyhfm/LYVGo6lYdnCwx6wp482YUe3ozrxmZWWFAH8/M2VDRPXV2EE94OxoL8TWbtqB3Lx8M2VUc6WlZVi2djNCQ0MxYcIEeHl56W2Tm5uL3oNG4l8ffmYxM1M+SrlSidS0DCEW4KPfH0xERERERERE9QuLG4mIDPDbklUoKSmtWJbL5Xj+6almzOjR4hNThGU/Hy8o5HK90fqcnBzrMi2jCvL3FZZTM7JQrlSaKRuix0vf8DA08vcWYvsPH8ft2DgzZVRzsXEJ2HswCq6urhg9ejS6dOmidw1Uq9X46Ksf0X3wJOQXFJopUyIiIiIiIiIieqxotSZ4aWr/gtaAV318L+rgZdhJP6EvQ94yc31WqOQ86uiczUkiqfRVVFyCXxevEDadNWU8nJydxG0NaltqwAvVfsnkMmFZrVEblrchDDpnQ1+Vf9bVftXkTdd5SSS1f1ksg99vgxo38GVI0+b5jhkjbzs7W0we3kcIl5SWYunqjUKhd322fuufyM7JBQC4uLhg1KhRePapyXp9ohqNBv/55EssXbHGDFka383bd4TPSALA39vNRH8rmIkh139Df7YMup6Z+XeXufI2+HrGl+W8DGC277eBeVPN8bOqhSfw3+aGssh7ZkRERCxurJRWq+WrDl9kWQzrbHi8biCo1Gr88NtSYdPxI4fA19fbeP+wlMoMe1Vy4yw+SZy5MfCvQkC5XLewxzJuylfG39cHD3ZwaTRaJKdmVvN7+iQy488X1S0j3NSUSqWYNrq/cM3QarVYvHIDCgqLzHVm1VZYVIxFK9ZX/A0ilUrRuVMn/LluIdqHtNbb/kz0ZXw9/w8DbtwY8DLkwTRLflnqQ3VUC+b++9ASX1Rj5uwMZOcUVZchv3/4u6vmzPm+8bMgIiIiIiILtHT1BmG2MolEgpfnPmW+hAwgl8uFZbVazWcWiKhSbVsEo3NoCyF25fotrNm4vd5fN3btP4wjx08LsebBAZj/6T/xyQd/h4eHh94+p8+er6PsTOvwsVPCsr+PO2ysFGbKhoiIiIiIiIiqi8WNRES1tHHbLiQkJQuxV597yjzJ1EBVxY26I/SpVKo6y8nYrK2t4O3pLsTik9PMlA3R48fbowGGRXYTYhlZ2fjhtyUoKS2tYi/zKy0tww+/LUF6ZpYQH9KnCyJ7dMaxP1fhg3df1nu4ITMrpy7TJCIiIiIiIiIiIiKqF7RaLb77eYEQGzKgD5o2bmSmjAwj1+kPBe4WOBIRVWb8kAg4O9oLsYNRJ7F5x556W+B4+NgpbNy2W4hZKeSYNrIvpFIJJgzvh9GjR8PJyUnYJsDfry7TNInsnFxcvHJdiHVv38pM2RARERER1SGDZ8l+jF5EZLH4E0xEVEvf/iR25HXp2A5dOoaZKZvqKS4uQWa2WKRzf+ZG/ZFKLVnAX+d1T3wSixuJjKlveBga+XsLsfjEZMz/fWm9LHAsLS3D/D+WIi4hSYgH+XmhX4+OAACFQoH/e/slNGkUKGzTukXTOsuTiIiIiIiIiIiIiKi+2HPgCK5cuynEXpk7y0zZGE53sFfAsgd8JSLTsrO1wYwxAyCTiY/X/bnvMNZt2VnvChz3HorCinVb9OKThvWGewNnAECgryeC/DxRWFgobNO+XUid5GhKR46fFj4TGysFOrVlPy8RERERERGRJWBxIxFRLZyNvoQjx08LsVeefco8ydRAdl6+sCyRSODj5QHg8Zq5EQD8fLyE5ezc/Cq2JKLakEqlmD1xCBq4OArx27Hx+N+PC1BQWGSmzPQVFhXj258X4lZMnBB3dXbEnElDhQ7JgoJCXL8VK2zXIbR1neRJRERERERERERERFSffKcz2GvL5k3Qv09PM2VjON3BXgFArdaYIRMishQtGgdg5tgBkEjE+L5Dx7Bo5XqUlpWZJ7EHqNVqbNi6C+s279RbN2ZgD3Rp11KI+Xk4Q6MRr30B/v4mzdHUVCoVjp44I8Q6hzaHjbWVmTIiIiIiIiIioppgcSMRUS189/NCYdnH2xPjRg42TzI1oFSKBYsKubyiE0+uU9yo1lh2R56tjY2wrLTwYk2i+sjV2REvzxwNRwc7IZ6QlIIv5v2CW7FxVexZd27ficeX837Vm7HRwd4WL88cjQYuTkI8+vI1YURPmUyGkFbN6yRXIiIiIiIiIiIiIqL64lbMHWzbtU+IvTJ3FiS6FT4WRHf2NcDyB3wlItPr0KYZpozoqxc/eSYaH301Hzd0Bk6tS4nJqfjs25+x+8ARvXWDe3VC3/AwvXhJkThro5OTE46cPKO3nSU5dOyU3uC7PTtyAFsiIiIiIiIiS8HiRiKiGkrPyMSKdVuE2Auzp8HKqv6P+KZUKoVlheL+6KSP28yNuiOv6hZ2EpFxeLq5VlrgmJmVg2/m/4G1m3agvLy8zvMqVyqxbstOfP3D70jPzBLWOdjb4qWZo+Hl0UBvvzPRl4Xl1i2awMbG2qS5EhERERERERERERHVN78sXC4MBuji7ITpE8eYMSPDVT5zo9oMmRCRpQnv0Bozxw6AVCoWeGdl5+B/Py3Aqg3b6nQWR7VajW279uPT//2ExORUvfUj+4VjWGTXSveNvnxdWPbw8MCpsxdw4fI1k+RqaumZWdi0fY8QaxLkA19P/b5gIiIiIiIiIqqf9O/cEhHRQy1ZtUEo1LGyssKzMyebMaPq0zzQAQkAEsn9GnfdzjxLL27UHTVW99yJyHj8vNzxxuxx+G7hBuTkFVTEtVot9h0+hktXb2DaxFFo0iioTvK5fSceS1ZtQHpGlt66e7NNVlbYCABndYob24dyRE8iIiIiIiIiIiIierIolUosXrleiD09bQLs7e2q2MMy6A72CgAqFjcSUTV1Dm0BG2srLFizE+U6gysfPHoCl67ewKgh/dGubctKrzfGoNFocPXGbWzavrvSokapVIKJQ3ujR8c2VR7j7AWxP9TDwwMAsHztZjRuFAh7O8u51ms0GixdtVFvoO+hvTuZKSMiIiIiIjOQSAAJ5zyDznPjRGRZWNxIRE80tVqDwuJiqFRqKJUqaLVaKBRyyOUy2NnYwMpKIWyv1WqxYNlaITZh1BB4erjXZdq1pnhIAaNcLt5cV6s1dZKTqegWZz44SyURGZ+nmyvenDMev6/ajthEsSMtPTML38z/A2EhrdErvDOaBAfpFSAbSqvV4nZsHA5FncKZ6EvCaNL3BPl7Yc7EoWjg4lTlcXRnbuwQWnXHHxERERERERERERFRfaRWq5Gclon45DTEJ6UiITkNBYXFUKpUUP5VkKNQyKGQy+Hs6IAAXy8E+nsj0M8b3h7u2LnnINLSM4RjzpkxyRynYlQSiQQymUyYrdHSB3wloroV0iIYf39hCpZu3IPbccnCuqzsHPy+dDWcnRzRvUsH9OjaES7OVfdL1kRhUTGOnTqLI8dOIyMru9JtfD3dMH10PwT6elZ5HKVSWenMjQCQX1CINRt34KkpY42Sc104ePQkbsXGCbGIjq3RrKGfmTIiIiIiIiIiotpgpQcRPTHUag1S0jORkJyK+KS7HXmJqekVHXi6JBLAy90NgX5eCPTzRqCvF1LT0nD52g1hu6enTaiL9I1Ct8CvXKmEVqut6Mh7kKV35JXrjMynkJtmZEQius/V2RFvzBmPfVHnsGXfMahU9x8O0Gq1OBt9CWejL8HHyxMR4Z3RuUMIbG1sDGqztLQMJ89ewKFjJ5GcklbpNnKZDEMju6Jv9w6QyaoeoaioqBjXbsYIsQ6cuZGIiIiIiIiIiIiILEBWTh6OnL6Ia7fuICk1Q7hHX5mS0rKK/WLikyriCoUc+/buFbbt0jEMLZs3NX7SZiCXyYXiRjVnbiSiGvJ0c8Frs8bi4IlobNoTpffMSV5+AbbvPoCdew8hpHULhLVthcAAX3i4NYBUWr3ZVLRaLbJzchGfmIyLV67j9PlLVT7DIZVKMKBHRwzq1emRz0VcvRGDsrJyIebufn8w75NnoxHatiXC2raqVp7mlJqegY3bdwsxNxdHjOzfzUwZEREREREREVFtsbiRiB5rGo0WN2LicOjEOVy+EVNlIWNltFogNSMLqRlZOHn+CgDg0KGDwjYNA/3Rq3sXo+ZsSvZ2tsKyRqNBdk4e3Bq4QK4zq6Old+Rl6oxWqHvuRGQaUqkU/Xp0QJvmjbB0w269WRwBICUtHas2bMXG7bvQvEkwAv19K15Ojg4PPX5BQSHiE5MRn5SM+MQUXL8Zg9Kysiq3D/LzwvQxA+Dj6fbI3KMvX4NGc3/WWqlUipBWzR+5HxERERERERERERGROWg0Wly9GYuDJ87j8o0YaLWGHzM/vwBXrl4TYkMGRlYMmGrpdAdBfFQRKBFRZaRSCfp0a4fWzRpWOosjcPd5jPMXr+D8xbvPm9hYWyPAzweB/r7w8faEtZVVxQDVKpUK5eVKpGZkIj4xGQmJySgqLnlkHtWZrfFBZy5cFpYD/X3g6uKEktL7BY8Ll6+D4zN2aBLcsFrHNIfs3Dx8/8tiKHUGvZ42sg9srBRmyoqIiIiIiIiIaovFjUT0WCouKcXxsxdx6MR5pGdmP3qHalCpVLh585YQ69ypAzKyc+Hl3sAobZhaAxdn2NnaoLiktCIWn5QMtwYu+jM3qi175sb4RLHzwN+7ejfzicg4vD0aVMziuG3/cZRXUlxeVlaOC5ev4cLl+w9JuDg7wdvLQ+jMUypVKC8vR2p6JnJy86rVvkIhx5DeXdA3vD1k1Zy59Uy02JnXqnkT2NnZwihPgxARERERERERERERGUl5uRKHTp7HoRPnkZldvfvm1XXjxg1hIEC5XI64lBz855vf0Ktre3TvHAqF3HIfNXncBnwlIvO6N4vj2cs3cehENG7Hp1S5bWlZGW7G3MHNmDsGt+vn5YaIziHo0q7lI2drfNCZ6CvCcqd2bTFucASWbNhTEVMqlZj/xzK8OvcpBAX4GZyrseXlF2Dez4uQrdNvHNGpDZo1rH/5EhEREREREdGjWe4dZyKiShQUFmHLniM4ce5SjWZprI7Y2FiUl5cLMStbR/z769/QokkQRgyIQEN/H6O2aWwSiQSBfj64diu2IhafkISwtq0g1yluVKs1urtbDI1Gg8Qkcba4QD8vM2VD9OS6N4tjeIfWOH7uKg6fuoD0rNyH7pObl4/cvPxat+nRwBk9O4Wga1gr2NvZ1GjfszrFjR1CW9c6DyIiIiIiIiIiIiIiU7h1JxFL1u9ExiPutwOAWwNXBPr7IsjfF16e7rC2soK8YmBBJcrKypGSllExS1hWTi6uXRNnbWzUqBGsra2RlpGF1Vt248jJ85gxfigC/bxNcXompzfgq8qyB3wlIvOTSiXo2LYZOrZpiqTUTBw6dREnL1xHebny0TvXgFwmRVjrpojo1BaNArxrNZuubn9o+9BW6BLaAjdiEnAi+npFvLS0DN/+tBDPPz0VTRs3NDR1o8nKzsF3vyxChs4g54E+HhjVr6uZsiIiIiIiIiIiQ7G4kYgeG2cvXsPKTbtQWFzy0O0auLog0N8Xgf4+CPT3ha+3F2xsrKGQyyGRSKBSq1FerkRGZhbiE5MrXlu3bhWO4+fnB0dHRwDAtVtxuH57KfpHdMHQyO4Vs40BACTVHyVPj0Ra+32rEOivU9z41wyHj1NHXlp6Jsp0ClHvdrBW5+Z+zTsAjLErZ4Yji1CLDjIAsLOzRWR4GHp3bYfrMfE4fOoiLlyLgdZI33uJRII2zRshonMIWjQOglRauzzPXhA788JCWhkjPTMy4P01+LMxZ9uWyqBfImZql6iO1PL3DxE9whP7O5eIiIiIjMNS/5408N+Ylvp3tFn/bW2me1eGflZme8+qbre8XInNuw9j/7EzVZ6ejY01unRoh9DWLRDg7wN7O7tHttiu7f3/P3T0OH766SdhfYsWLYTl5LQMfD5/EQZEdMXgyHDjzOJoSH9qDcl1ZjhTawwY8NWg27oGfscM+Y4a2raZfgfUppDrQYb1S5nxOmqpl3BDWOZXFJAAfj4emDwiEqMGdMfJ6Gu4cjMO8cnpyC8srtUh7WytEejriRbBgega1hKODo++rldFpVIh+sp1IdY+pDUkUimmjuyHkrJyXLh2/1mS0rIyfP/rYkydMBKdwkIM/hk0VMydePy2ZLXeQLne7q54cdpQWFspzJRZLRh0DTdeGnXK4O+P8Z+fqhatgQPDW2ofl6X+u8ecLPWzNoQJnmt87Bnys/UkfscMZui1jO95jVT2/ebvk7ojkfK6DPA9ILJwLG4kIotXUFiElZt349yl61Vu4+PlgYjwzmgf2gaODvYPPZ6VVAorhQIO9nZoFBQAAIhPSMT7//q3sJ1uR55Wq8Wug8dx8epNTB87BA0DfGt5RqYV6CfOLhmfmAKtVgu5TsejWq2uy7SMKj4pWVh2cXaE0yM+dyIyPalUgpZNgtCySRBy8wtxPSYB8cnpiE9OQ2JKBsqrOeOulUIOfx8PBPh6IdDXE82DA+Dq7GhQbiUlpbhy/bYQ48yNRERERERERERERFQf3J2tcUeVszX6+XihV/cu6BjWFjbW1rVuZ/W6DcKyl5cn2oe1Q3pGphDXaLTYeeAYLly9iRnjLGsWR90+UUse8JWI6i9bG2v06hKKXl1CAQC5+YV/9YumIyE5HXkFRVCqVFCq1NBqtVDI5bBSyOFgZ4sAXw8E+Hoi0NcTbi5ORisqvHYzBiUlpULsXn+oTCbF0+MG4acVW3HtdkLFeqVKhYXL1+HchSuYNGYYnJ0M65OtjXKlElt37sPeQ1F6Rcrurk54afowONjZ1nleRERERERERGQ8LG4kIov2sNkapVIp2rVthYjwTmga3NCgG76Llq0SbpI6OTlizlPTcOb8JRQViyPspaRn4YuflqJ/z84Y2q+HcUYrNSLd4sai4mIkJKXojVKqUlluceOV67eE5UBfLzNlQkRVcXFyQJd2LdGlXUsAgEajQWpmDhL+GrlU9UBnnpVCDrlCDid7OwT4esHbwxVSqXFH2Ym+fE0o6pZKpWjXpqVR2yAiIiIiIiIiIiIiqqn9UWewdvu+Sic8aBQUgDHDBiC4YaDBxS+lpaVYtmqdEHtu9kz8652XceN2LNZt3onE5FRhfXJaJj7/cTGmjh6Ebh1CDGq/rsh0+hcsecBXIrIcLk4OcHFyQEiLYLPlcCb6irAc6O8DdzfXimWFQo7npgzHgrU7EX01Rtg2+tJV3Iq5gwmjh6Jju7Z1NotjzJ14LFm1EWk6RfYA4OvZAC9NGwZnRw50TURERERERGTp6lfFDRFRNWm1WuzYF4Wte49Uuj60TUtMHD0ULs5OBrel0WiwcOlKITZ5/BhMGjMMY4cPxJ6DUdi++4DQ8aXVarHr0AncSUzBc9PHGDRCqrG5NXCBq7MTcvLyK2KHok4iOFCcadJSO/IKCgpxLvqyEGvaKMBM2RBRdUmlUvh6usHX063yDUzcQXb2gnjdaNE0GPb2diZtk4iIiIiIiIiIiIioKlqtFtv3RWHbvii9dQq5HMMH9UVkRDejDQa4aesO5ObmCbGZUydCIpGgeZNgvPvqXPy59xC27zkIjUZTsY1Go8WSdTtQUlqGyO6djJKLKenP3GiZfaJERDV1Ruc5ivYhrfS2UchlmD1+MFZu3Y+os2IxZFFxCRYsW4vT5y5iUN8INAz0N1mRY3pmFvYfOoZDx07pzdYIAMEB3nhu8hDY29afZ3GIiIiIiIiIqPZY3EhEFker1WLd9n3Yd/S03jp7OztMHD0UHdq1MdpN1MNHjyEm9o4QmzV9MgBAoVBgcL9eCG3dAotXbUB8YrKw3Y2YeHz3+yq8+NR42NvZGiUfQ0kkEnTvHIatuw9WxE6du6g3u6FKparr1Iwi6tRZqB4ozJTLZejSTv+mPBHRg6rTmUdEREREREREREREVBe0Wi027TqEXYdO6q1rFBSAGRNHw8vT3ahtLliyQliO7NUTjRoGVSzLZDIMGdAHIW1aYvHK9XqzOK7dtg/lShUG9e5m1LyMTSaTCcuW2idKRFRT5y6IxYp5+YXILyiEk6ODEJfJpJg6si9aNgnCqm0HUFhUIqy/eOU6Ll65jgA/H0SEd0bHdm1hbW1lcH4ajQaXrt7AoaiTuHL9VqXbyGVSDO3TGX27tYNMJkWl0xoTERERERERkcVhcSMRWRStVos1W/fiwLEzeutC27TE5LHD9W68GuqPxcuF5ZYtmqFzx/ZCzNfHC2+//Ax27T+iN4vjncQUfPv7Srw2ZzLsbG2Mmlttde8chu17D1eMqqpUKnHp6nVhG5Xa8jryNBoNDh8Ti147tG0BB86+RkSPcPGyeA08evIsjp06h26dwsyUERERERERERERERE9qf48dKLSwsahA/pgcL9eRput8Z6ExCTs2ntAiN0b7FWXv6833n11LjZu3429B8VZJTfvOgQ7GxtEdK2f99Zvx9xBYlKKEHuwX5eI6HFWrlQKy/uPnEBor5FY/MNn6Nm1g9727Vs3QdOGfli97QDOXtYvNkxISsGyNZuwfsuf6Nw+BE2CGyLQ3xfubq7VHow8L78A8YnJuBOfiBOnzyNbZwbhBwX5eWL6yEj4eDao1rGJiIiIiIiIyHKwuJGILMq2vUcqLWycMGooenXvbLTZGu8pKCjE2o1bhdjT06dU2o5MJsPgfr3QukVTfP/rYhQWFVesS0xJx4+L1+LlWRNhZaUwao614ezkiHZtWuDsXyPz5eXlYd2m7ejRtUPFuWk0ljfC3eVrN5GdkyvEIrq0M0suRGRZdIugY+MS0XPoFPzj9efw/pvPQ6Ew/7WbiIiIiIiIiIiIiB5/R09fwOZdh4WYRCLBxNHDEBHeySRtLl6+CtoHZr9ycnLEmJFDq9xeJpNh7PBBcLC3w6bte4R1q7bsgoO9Ldq3bWGSXGvrzLkL2PbnnorBX+9xNPLAuURE9dWsKWNw6txFIRaXkIzeI2fg7Zdm49/vvKQ3A6OjvS1mTxiMsMu3Kp3FEQBKSktxMOokDkbdLcq3tbVBoJ8PAvx94eLsBCuFAnKZDGqNBuVKJYqKipGQlIL4xGTk5Rc8Mm+92RqJiIiIiEgkldx9Pen4HhBZNBY3EpHFOHo6Gtv3iaN/SqVSzJg0Gp3bh5qkzdXrN6K4+H6Rokwmw7RJ4x66T6C/L954cTa++3kRcvPyK+K345KwYPUWPDt1tNGLMGsjoltHnL1wBRkZGYiLi4NWq0VicioC/HwAAL4+3mbOsGbUajW2/rlPiAX4eqGhv4+ZMiIiS/LJ/72FfmOeQtEDhekajQYffjUfO/cewpIfP0fzJsFmzJCIiIiIiIiIiIiIHnfpmTlYvXWvXnzahFHo1sk0syFqtVosXLpSiE0cOwp2dnZV7HHfwMgIKBQKrN2044HjAcs27ERwoB9cnB2Nnm9NabVa7D1wGEeO6c+EGRTgj7atW5ohKyKiujd35kTY2drg5b99iILCooq4VqvF5/N+w5/7DmPJ/M/RpmVTvX3bt26CFsH+OHrmMg6fvoSsnHy9be4pKSnF9VuxuH4r1qB87Wys0S2sBSI6t4W7q5NBxyIiIiIiIiKi+o3DGRGRRcjKycParWLhmkQiwVOTx5qssBEAFixZISwPGdgP3t5ej9zP29MDrz//NJx0RvqMvnITJ85dMmqOtdWkYQCKCwtw586dipFYr1y/hdLSMgT4+6JfnwgzZ1gzu/YfRkJSihCL6NKuXhSSElH916VDKM7t34iuHdvprTt9/hLaR47BD78vE0auJiIiIiIiIiIiIiIyFo1GiyXrd0CpVAnx8SOHmKywEQCORB3HrdtiAcrTM6ZUe//Int0wbEAfIVZSWoblG/80+z11pVKJtRu3VlrY2KZVC0yfPA4ymcwMmRER1T2JRIIZE0ch+uAm9OzaQW999OXr6DRgPL75aZHeLLcAYGdrg/49OuCDV6bj+anD0aZZQ5jicYwgX09MGxmJj96ciTEDu7OwkYiIiIiIiOgJwOJGIqr3tFotlq7fibLyciE+eexwdAxra7J2r9+4haM6HV2zpk+u9v4e7g3w8rMzYWdrK8TXbN2L3LwCo+RYWyqVCus3b4etlUwvnpGdi+mTJ8DGxsZM2dVcUkoatu8+KMR8PN3QuV0rM2VERJaoSXAQDm1dhv/8/VXI5eIE5yUlpXj5b//FkInPIDk1zUwZEhEREREREREREdHj6uDxc7gdlyTEeoV3Rp+eXU3a7h+LlwvLLZo3RZdO+kUvDzO4f290CgsRYpeu3zbroK9FRcVYvHwNLl+9rreuZ3gXjB05VK8vgIjoSdAw0A/7Ni7CZ/96CwqFQlhXVlaON//vMwwYP1tvcOl7pFIp2jRriOenDscHr87A0D6d0apJIBzsbSvd/lGsrRRoHOiDyG6heOeZsXjn2XHoFtYCVgpeo4mIiIiIiIieFLwLQET13uGT0bgeEy/EunfpgB5dO5q03YVLxVkbPdzdMXRQ/xodw8/HC9MnjsLPC+8f695Ipc/PGGuWWQWLi4uxYu1GJCQmw8vDDb7enkhOTQcA+Pj4wMrGDmeiL5t0BFhjUqvVWLxyPdRqdUVMIpFg+pjBULBDkohqSC6X459vvoBBkT0x/fm3cf2WOFr1n/uPICRiBH7+6j8YO3ygmbIkIiIiIiIiIiIiosdJemYONu0+LMTc3VwxaugAk7ZbWFiINRs2C7FZ0ybXuA9TIpFgwughuH4rBvkFhRXxNVv3okXjhnBxdjRKvtWVmZmFZas3ICc3V4hLpVIMG9QP7duFVL4jEdETQiaT4e2XZmNA7+6Y/sI7uHT1prB+3+ETCO09Gj989j4mjxla5XHcXZ0xpHdnAHcHLs/NL0R8cgbik9ORnJ6F0jIlVCoVlCo15DIp5HI5rBRyeLm7IMDXE4He7vB0c4FUWvfPzhARERERERFR/cGqDyKq17Jy8rBh5wEh5urijDEmLihRq9VYvHy1EJs2aRysrKxqfKzQNi3RqX0ITp29UBG7N1Jp1/amm3myMllZ2Vi2ej2yc3IrYq2bN0F2Th68fXzg4eEBAFi7aQeaBgfB3a1BneZXG9t3H9AbMbB/z85oGOBjpoyI6HHQMawtzuzbgHf/8yV++G2psC47Jw/jn34VMyaMxLef/BPOTnX7UAYRERERERERERERPT60Wi2WbdwFpVIlxKdNGAVr65r3TdbEmvWbUVRUXLEsk8kwffL4Wh3L3s4OU8aNwE8L7s8EWVJahhWbduH5GWMNzrW67sQlYNW6TSgpLRXi1lbWmDBmOBoHN6yzXIiI6rvQNi1wctcavPfRN/jmp0XCuty8fEx97m1s+XM/fvjsfbi6OD/0WBKJBK7OjnB1dkRoy+DqJaDV1DZ1IiIiIiK6RyK9+3rS8T0gsmj8CSaiem3Vlj0oK1cKsanjR8LWxsak7e7asx/JKalCbNb0ybU+3viRQ+Dk6CDE1mzdi6Liklofs6bi4hPx2+LlQmEjADg62ONvrz9fUdgIACWlpfjul0XIyy+os/xq4/CxU9ix56AQ8/F0w9DIcDNlRESPEzs7W8z79H3sWPUrfLw89NYvXr0J7XqPxKGoU2bIjoiIiIiIiIiILI+2kpc509Ga72W+k7bQl6GnbamftRnfszp0Oy4Jt+4kCrFe3bugWeNGJm97wdIVwvLgAX3h4+Nd6+OFtG6BTu3FWREvXruFpLQMQCJ5yEta+xckFa8Ll65iycq1KCktE+LOTk6YPXMKGgc3EuJ3X+Zi4HXBIq/BgP77X5OXgS1LJLV+mZch75mBr4f+3D7iZc5zNiRvg87Z0Jd52NhY46v//g171i9AgJ/+INIrN2xHSK9R2HPomPHP25Drv0Rqvu+owd8VMzH0/bbU65FBL0O/o4a832Zktvfbgl+WylK/o0R1wtz3gsxwP+RJvA4SEVG9wb8wiaju1PCP3dSMbFy6HiMconuXDmjVvInJU/1jyXJhuWP7dmjbplWtj+dgb4fJY4cLsZLSMhw/e+kR74OhN7sASIALl69g8co1d0co/SsGCeDs7ISnZ0zC0IGR6BTWRsgvMysH835ZhIKCwlqftymdOBONleu3CjGJRILpYwZDobDAiYkt+gabxEJf5jxnc7VtRmb/ntbewL4RuHB4C8aN0J81OC4hGX1GzcC7//nybjF+PcqbniQWel0gqi6LfVCLiIiIiIiIiOjRDp04Lyw3cHXBqCH9Td7uzVu3cfjocSFmyGCv90wYNQQO9vZC7NDxcwYf92G0Wi0OHonC+s3boVaLs4D5enthzsyp8PRwN2kORESWLrJnV0Qf3Iip44brrUtKScOAcbPx2nsfo6SktJK9iYiIiIiIiIhqj8WNRFRvHT55Xli2t7PDmGH6hSXGlpWVjc3b/hRixujIC23TEu3aigWSh06cg0Zjuoeu73bkHfurI08trPPx9sKcmVPg5Xl3NrJJowbDz8dT2CY5NR1fzf8d2bl5JsuxNg5FncTileuh1XlgfdzQvmgYoD+SIBGRodwauGLV799i0fzP9Gbi1Wq1+GLeb+g6cDwuXb1hpgyJiIiIiIiIiIiIyNLkFRTh/JWbQqxPz66wtrYyedsLl64Ult3d3TBs8ACDj2tvZ4ee3ToKsZPnLv01m6LxqdVqbNq6E/sPRemta960MZ6aNhGOOvf1iYioci7OTljy4+dY8ctXcHVx1lv/3S9L0G3wJOTUs2dIiIiIiIiIiMiysbiRiOqlsvLyu7MaPqB7l/awtbUxedvLV69DeXl5xbK1tTUmjx9jlGNHRnQTljOycnDt1h2jHFvX/Y68o3rrmjdtjFk6HXm2tjZ4ec5UeLg1ELZNz8jCV9//hluxcSbJsyaUKhU2bN2Fleu36hU2DunbA326dzJTZkT0JJBIJJg+YRSiD21GRDf96030pWvoMmA8zpy/VMneRERERERERERERESiY2cuCjMNKhQKdOsYZvJ21Wo1Fi1bJcSmThgLKyvjFFX26NoRUun9x1HKypU4ec74985LSkqwZMVanL94WW9d107tMXHsSKOdExHRk2Ti6CG4cGgT+vXqprfuwuXr+PrHhXWfFBERERERERE9tljcSET10unoq8LonRKJBD261k3h2oIlK4TlUcMHw9XVxSjHbtwwEL4+XkLs0ImzRjn2g0pLS7F05bpKO/K6dKy6I8/J0QGvPDsVbjrnm5Obh2/m/4G1m3YIhZ91KS4hCZ9+8xN2Hziit65vz84Y2reHGbIioidRUIAf9m5chM/+9TYUCoWwrqSkFD8vWlXFnkREREREREREREREd6nVGhw5dUGIdQxrCzs7W5O3vXvvASQlpwixWdMnG+34ri7OaNuquRA7dPyc3uClhsjJycXvi1fgTnyCEJdIJBjcPxKD+kcKBZZERFQzfj5e2Ln6N3z78XuwsbEW1qWmZ5opKyIiIiIiIiJ6HPFOLhHVO1qtFodOnBNirVs0hbubq8nbPh99EeeiLwqxp6dPMdrxJRIJIrp1FmIXr95Cdm6e0dq425G3ErFxlXfkDR7w8I48N1cXvPnCTHh7ugtxrVaLfYeP4eOvf6zTWRyVKhU27diDL+b9ipS0dL31w/r1xJjBkZBIJHWWExGRTCbD2y/Pwcnda9G4UaCwzlxF4ERERERERERERERkOW7ExiMnr0CI9QrvXMXWxrVgqTjYa/t2IQgNaWPUNnTPJSU9E3GJqUY5dmJSMn5bvByZWdlCXKGQY9LYkejSqb1R2iEietJJpVK8/Mw0DO7bU4j7eHmYKSMiIiIiItIjkfJ170VEFos/wURU72Rk5SAhWSxii6irjjydWRv9/XzRt0+EUdvo3D4ENtb3R7XTarU4d+m6UY59tyNvBTIys4S4QqGoUUeei7MT3nhuJho3DNBbl56ZhW/m/4GlqzciKSXNKHlXRqlU4sSZaHz6zY/4c+8haDQaYb1UKsWE4f0xpG8PFjYSkdmEtmmBsLYthZiPt6eZsiEiIiIiIiIiIiIiSxEbL86cGODng0B/X5O3m52dg41bdggxY87aeE+zJo30Bq+NTUgy+LhXrt3AwmWrUVRULMQd7O0xa9okNG/WxOA2iIhIlJQiPsPjy/5QIiIiIiIiIjIiubkTICLSpTtip5OjA1o1N30nVHl5OZatXifEZk6dCJlMZtR2bGysERbSCsdO3Z+d0hijlF69fhPrNm2DSqUW4g729pgyYTR8fbxrdDwHezu8PncG9h05gS1/HoBSpapYp9VqEXXyLKJOnkXjRoGICO+MsLatIJcb/mslMysHR46fQtTJsyjU6ZS8x9/HCzPGD4W/j5fB7RERGSo5VezM8+O1iYiIiIiIiIiIiIgeIT5ZHES0SXBQnbS7Ys16lJeXVyxbWVlhyoSxRm9HKpWiccMgZGblVMQSkmrfJ6rVanHs5Gns3ncIWq1WWOfl6YEp40fD2dmp1scnIqKqsT+UiIiIiIiIiEyJxY1EVO/EJ4udWkEBfpBKTT/R7JbtfyIrK1uIPTVtkknaahjoLxQ3xiemPGTrh7vfkXdYryPP08MdUyeMqXVHnlQqRb+IbmjbsikWr96C2PhEvW1ux8bjdmw81jrYo0O7NggK8EOgvx+8PNyq9bmVlJYiITEF8YnJuH4rBleu39I7jwfzGRLZHQN7dzN60SkRUW3pjlTKzjwiIiIiIiIiIiIiepQEneLGupi1EQD+WLxcWB41fDAaNHCtYmvDBPr74sSZ8xXL8UlpVW/8CLv3HUTUidN68caNgjBhzAhYW1vX+thERFQ1tVqNlLQMIebHmRuJiIiIiIiIyIhY3EhE9Y7uLIbm6siL6NENTRoHm6Qt3XNKz8pBSWkpbG1sanysw1EnsO/gUb1440ZBGD9mOGxqcUxdXh7uePP5mZXO4nhPQWERDhw5UbFsbWUFfz9v+Pv6wM7WBgqFHFKpFCqlCuUqFbJzchGfkIz0zKxq5cDZGomoPtJoNHojlfqyM4+IiIiIiIiIiIiIHiK/sAi5+YVCLNDP9H2iFy5extnzF4TYrGmTTdZeoL+PsJySnonyciWsrBQ1Ok56RmalhY0dwkIwZGA/DopKRGRC6RlZUKvVQoz9oURERERERERkTCxuJKJ6RaPRIlFnBqy6KG5MTknFzt37hJgpO/J8fbwgk8mEG8AJyWloFhxUo+NotVocPX5KL96+XVsMHdgXMrnxLvP3ZnHsGNoah0+cxdGT55BfUFjl9mXl5RWzOhqieeMg9OzSHqGtmkEmM/0MnkRENZGVnQulUinEOHMjERERERERERERET2M7gyG1lZW8PJ0N3m7C5asEJb9fH3Qv29vk7Xn7+sDiUQCrVYL4G7fZkJKGhoH+dfoOLm5eXqxfn16onvXzpBI2X9IRGRKyTqzNspkMnh6uJkpGyIiIiIiIiJ6HLG4kYjqlYysbJSWlQuxuihuXLJ8NTQaTcWyvb0dxo0ebrL2FHI5fL09kZCUUhGLT0qtcXGjRqNBmc771btnN/Tq0Q0SicQouepycXbC8AG9MTiyJ6IvX8Oh42dwMybOqG3Y2lija/u26NmlPbw9eVOciOqvpBTxARSJRALvOngAhYiIiIiIiIiIiIgsV0p6lrDs7+sNqYmL9MrLy7F01VohNnPqRJPOemhtbQVvTw+kpN0f3DYlLbPGxY26XJyd0aNbF0PTIyKiatDtD/Xx8jD57ywiIiIiIqoBiQSQ8G90mOi5eSKqGyxuJKJ6JT0rR1h2cnSAs5OjSdvUarVYsFQcpXTCmJFwcHAwabv+fj5CcWN6Zs5Dtq5cZQWMbVq1MFlh44Pkchk6hLZGh9DWSE5Nx/lL1xCflIL4xBTk5hfU6FgymRS+3p4I9PNG46AAhLVpBmsrKxNlTkRkPLqdeV4e7pAbcdZcIiIiIiIiIiJ6jGi1d19i0LBjmu2BDQPb1XsfngAGfVbmfL/M+Fmb8z0zZPdq5K072Kuzs5MBDVbP1h27kJkpFlU+NW2Sydt1dnYUihvLypQw+HtVXxjyPbHk66AhP5sGnbf5vjeGnbIFf9aGvOdm/b1npu+ooV9Rs35Vqm5ctz/Uz8dT3MBs1wTAsDfdQv++MuuPlgEPzGs1j97m4Y0bsK+5/h6FgdcUCy1QMPizJnqMseiGiIiIqEr15MlrjXH/UWPWPwD5xyeRIcrKlcKyvZ2tyds8duIUrt+4JcSenjHF5O3qnlu5zrmbX/VvsPl6e8DX26NiOT8/H/FJqYhPSkVmdi6USiWUShXUGg0UcjkUCjlsbazh7+OFQD9v+Hh7QPFgMZBGzZtddcmcN2PN6kn8e8Gc52yp3xM89DuenFpJZ56wvaV2qBERERFR5fg3GhERERERGU6pUgnLVgrTP7qxYIk42GuP8C5o2qSxydu1UiiE5XJVzftEdQd2texiMSIiy5Kcmi4s+3l7mSkTIiIiIiIiInpc1ZPiRiKiu1QqtbBcF7Nf6XbkNW0SjO7dupi8Xd2OPN1OzNqqD515To4OaNOiCdq0aGLuVIiITEp/pFJ25hERERERERERERHRw+n2icpkMpO2l5KSih279gqxWdMmm7TNe+Q656Z77kREVL8lpYjFjT7enlVsSURERERERERUOxY6dz0RPa7Uap2OPKlpL1NFRUVYtW6jEHtq2iS90T9NQSYTz0333KujLvIkIqKqsTOPiIiIiIiIiIiIiGpKv59QY9L2lqxYI/RF2tvbYfyYkSZt8x6VTh+obrFjdejN3AjzD/ZKRPQkKCwswuFjp4UYB3slIiIiIiIiImPjzI1EVK/oztSo29llbOs3bUNBQWHFslQqxYwpE03a5j2mmqWyPszcSET0JEhOTcPmneJI137e7MwjIiIiIiIiIiIiooez0u0TValM1pZWq8WCpSuE2PjRI+Do6GCyNh+ke27G6hMlIiLTe+nd/+L2nXghFtKqmZmyISIiIiKiykkATpYDgO8BkSXjzI1EVK8odDqzlCbsyAOAP5YsF5YH9O0Nfz9fk7Z5T7lSKSxbKWrekceZG4mIzEOlUmHq3LeQkZktxHt0bW+mjIiIiIiIiIiIiIjIUigUCmG5sKjIZG2dOHUG167fFGKzpk82WXu6CouKhWVj9IlysFciItNbuGIDFq/e9P/s3Xd803X+B/BXkqZ775UO2kKh7E3ZIBtBmSrgyamn3p36O09v6+9OvfVznd6dnuPEjUUZyl4CBcreFAoUSpvuvVfW749C4JO00DZJv0n7ej4eeejnne94f9MkLXnn/fkIsSED+mL65LESZURERERERERE3RWbG4nIrri6Ogvjqqoa6PV6m5zrWtZ17E09KMS6spBXWVUtjF1cnNvYsmNYzCMisr2XX38X+9KOCbF7Z0zGxOSREmVERERERERERERERI4iONBPGKvzCmxW4/v4M3Gy17heMRg/doxNzmVKq9Uiv7BYiIUE+XfJuYmIqPPSM67gZ79+WYh5eXog5aO3uAIvEREREREREVkdP20gIrsSHhIojBubmlBSVo6QoMA29ui8T79MEcZ+fr6YN2em1c/TlpzcfGEcERrcqePIZDI2NBIRdaGdew/iz2++J8SiIsOx6p9/5Yq6RERERERERERERHRXUeEhwriuvgHlFZUI8PdrY487MxgMqKurQ1l5BUrLylFaWobSsnKUlZcjZe0GYduVKx7sss+yC4pKoNVqhVhURKjFx2VtlIjIdurq6rHk0V+goaFRiH/41iuI7xUtUVZERERERERE1J2xuZGI7Iqvtxe8vTxQXVNnjOXk5ne4uVGj0aD8RvGurLy8pYhXVo7SsjKUlVWgtKwMm7fvEvZZtnQhXF1drXIdd9PQ0IiS0nIhZo1CHsBiHhGRLeUXFmH5Uy8I77VOTk5Y/cEb8PfzlS4xIiIiIiIiIiIiInIYAX7ecHdzRf1tjSM5ufntbm4sLCzCH17+K46fPG2sgzY1Nd11P5lMhocfWtrpvDvKdLLXQH9fuLu5dfg4nFiQiKjr/PzXr+Di5atC7KmVD2LJfbMkyoiIiIiIiIiIujs2NxKR3YmKCMX5jFsflObk5mPEkIHt2jc3Lx8PPvITHEg70uHzrlz+YIf36aycPLGQp1DIERbSudUpuXIjEVHX0Ol0WP7kC2bN6X/5wy8wZsQQibIiIiIiIiIiIiIiIkcjk8kQFR6MjKs5xlhObj6GDExq1/4rn3wG23b+0OHzTpsyEarIiA7v11mmzY3WmuyViIhs45PV6/FpygYhNrh/X7zx8q+lSYiIiIiIiIiIegS51AkQEZmKCg8Rxjnq/Da2NPfrP7zcqcbGgf2TMGRw+xoorcG0kBcRGgylE/vNiYjs2cuv/xt7Dx4VYnOnT8JzT62UKCMiIiIiIiIiIiIiclQqk5rotWx1u/c9dvJ0h88nk8nw2+f/p8P7WSLL5Jo629xounIjJ34lIrK+9Iwr+NmvXxZiXp4eSPnoTbi6ukiUFRERERER3Z2MN+ONiBwVO2mIyO6YFrVycvPR1NQMFxfnu+6blZ1z121MyWQyvPbn/zUritlS5rVsYWzJLKWmabOYR0Rkfbv2peHVN94TYqqIMKz6518hl3O+ECIiIiIiIiIiIiLqmJjIMGGceS0bpWXlCAzwv+u+nh4eKCsrb/U+JycnBPj7ITAgAIGB/ggM8EdIcBCWLJiPiePHWiX39lDnFSA3v1CIRZtcMxER2Ye6unosfewXaGhoFOIfvPkyEuJipEmKiIiIiIiIiHoMNjcSkd2JVYVBJgNu9ug1NTfj2KmzGDd6+F339fP1MYv5+HgjMMAfAf4txbuWWwACAlqKetOmTERsTLS1L6NN5ZVVOH/xshCLjQrvsvMTEVHHFBQWY/lTLwjN405OTvj6wzcR4O8nYWZERERERERERERE5Kj6JcTAzdUFDY1NAFomMN1/6Djunzv9rvv6+/kiO+fWqoh/+PVzeGT5AwgMCIC3t1eXTuraltS0o8LYx9sT8TGqTh2LKzcSEdnW0795FRcuXRViT658AEvvny1RRkRERERERETUk9hRc6MVP3y29INsmSWr7/BD9I6TvrBC9sXL0wNJfeJwPuPWB6epaUcxdtSwuxbi/P3EJpNfPvNTvP7XP9kkz846ePi4UHBzcXbGkP6J6Oxr4Y7FPEveDy3aV9/5fVsOING+lrLk/Yy/P7qcHRT2u1xPLfZbcN06nQ7LnnwexSVlQvwvf/gFxowY0p6Td/rcRERkBVL+7uuJf2sQOQIp3xdaO3dP/RudiIioxzLA6p8XWfT3hAX/bumx/+Sx4MId9t+oluYt0WPmAP8ud3ZWYvTQJOxJO2mMpR09ibkzJkOpVN5x3wB/cXVHH29vxPWKtUmenVHf0IBjJ88KsXEjBkOhUNjgbBY+Ry3aXcqapoPWU6V8bUr0O9MB3o7aJF0jsYQPmoP+yrTk+f3J6nX45Ov1Qmxw/7548+XftjMpB/18y5IXp8XfTbTk3Jac14J9LT63Jd/HtJBF32GS8mftqK8tCX/WlrD0u26Oet3UcY78xx11UA/8LqhFn8NYLw0iIuqZ+Bc1EdmliaPEhpHc/EJkZavb2PoW0xW0yisqrJqXpbRaLQ4eOSHERg3tD1cXF4kyIiKiO3n59X9j70Fxdum50yfhuadWSpQREREREREREREREXUX40cMEsZ19fU4eTb9rvsFBNh3TfTI8TNo1miMY7lchrEjB3f6eFy5kYjINtIzruBnv35ZiHl5eiDlo7fg6srvsRARERERERFR12BzIxHZpb4JsQjw8xFi+9KOtrH1LQEB4iyl5RWV1kzLYqfPXUR1Ta0QmzBqqFXPwWIeEZF17NqXhlffeE+IqSLCsOqff4Vczj+jiYiIiIiIiIiIiMgyIUH+SIyLEmI/pB6CXn/nlWNMJ3wtK7ef5katVot9B48IsUH9+sDX20uijIiIqDV1dfVY+tgv0NDQKMQ/ePMVJMTFSJMUEREREREREfVI/FY2EdkluVyGCaMGC7GTZ9JRVFJ6x/38/XyFcVl5uZUz6zydTocde/YLsfhYFcJDgyw6rulMpUREZLnCohKseOpXQsO4QqHA6g/eMPvSCBERERERERERERFRZ403qYmq8wqw58DhO+7j72fa3Gg/NdGtu/ahuLRMiE0YzcleiYjszdO/eQUXLmUKsSceeQBL758tUUZERERERNQpMjlvN29E5LD4CiYiuzVm2AA4OSmMY51Oh89T1t9xptIAf3HlRnuapXTHngPIzS8UYtYu5AEs5hERWUqn02HZk8+bNdT/5Q/PIXmk9d+3iYiIiIiIiIiIiKjnGtAnDsEBYrPi91t23XHSV3tduTEnNx/bfxAne1WFh6J3r6g29mgfzvVKRGRdn369Hp98vV6IDUpKxFuv/FaijIiIiIiIiIioJ2NzIxHZLU8Pd0wYNUSIXbuuvuNMpWaFvDL7KOTlFRRhy869Qiw0OABDkvpYfGyu3EhEZF2vvPEu9hw4IsTmTJuIX/50pUQZEREREREREREREVF3pVDIsez+6UIDn0arveOkr6YTvpaXV9oww/bRarX4zCRnuVyO5Qtns55JRGRHLlzKxM9+/bIQ8/Rwx5r//gOuri4SZUVEREREREREPRmbG4nIrt07bRyC/H2F2J1mKjVtbiyvqJB8JUOdTofPvl4HnU5njMlkMjy8aC4UCsUd9iQioq62O/UQXnn9XSEWGR6KT/71N8jl/NOZiIiIiIiIiIiIiKwvPiYSE0e3Munr/kOtbm++cmO5zXJrr6279iG/oEiIzZw0BqrwEIuPbdocKXH5l4jIYdXV1WPJo/+D+voGIf7BW68gIS5GmqSIiIiIiIiIqMfjN7SJyK65ODtj+cJZQuzmTKW3Nwve5G9SyNNqtaipqbVpjnez/Yf9UOcVCLFpE0YhRhVuleObF/NYzSMi6ozCohIsf/IF4X1UoVDg6w/fNPuiCBERERERERERERGRNc2bNt5s0tcNW3bhwqVMs20DAsSVG8vKK2yZ2l2dOpuObbtThVhkWDBmTh5rleOb1kO1Wi00Go1Vjk1E1JM8/dtXzX6vPPHIA3jg/jkSZURERERERERExOZGInIACbEqTBozTIhdu67G52s2QK/XC/EAf7GQB0g7U+mxk2execceIRYaHIA5U8db7Rymqz9WVddY7dhERD2FTqfD8qdeMFsZ+M+//wWSRw6VKCsiIiIiIiIiIiIi6ilcnJVYsXAWbu/j0+l0+OCT1bh6PUfY1t/PVxg3Njaivr6+C7I0d+FSJj7+8lth4kAZgDn3jIeTk6LtHTsgwN8Pcvmtr7fodDpczbpulWMTEfUUn369Hp+sXifEBiUl4q1XfitRRkREREREZBUyGW83b0TksNjcSEQOYf4M85lKj544g2++2yoUyjw9PaBUKoXtpJqp9Gx6Bj79ep3ZSorDBvS1WiEPAMJDQ4TxpStXrXZsIqKe4tU338MP+w8Lsdn3TMTzP/uxRBkRERERERERERERUU8THxOJqeNGCLFmjQb//vBzXL6aZYy1NuFreUWlrdMzc+7CJby/6ivodDohbgDw8dffYc/BY9DrDa3v3AFubm6IUkUIMdZEzel0ejQ2NaG+oREajdasTk1EPdeFS5n42a9fFmKeHu5I+e9bcHV1kSgrIiIiIiIiIqIWTlInQETUHi7Oznhk6Tz846OvoNFojfF9B49Aq9XiwYX3Qi6XQyaTIcDfD4VFxcZtysq6fuXG46fP4ZOv1pqtLAkAm3cfgDq/CA/eNxM+3p4Wnyuxdzwyr103jq9czYJOpzNb0bGnamhsQkNjEzRaLbRaHRQKOZROTnB2VsLT3Q0yztRB1OP9sP8wXn7t30IsMjwUn/zrb8JM0EREREREREREREREtnbf9ImorKrF8bMXjbHGpib8+8PP8eiKJRiYlAhfXx/I5XKhFllWVo7IiPAuy/PoyTP47Ov1rdZDAUCj0eKbTbtw6vwlLF84G8GB5g2ZHZGYEI/r2Wrj+NKVq9Dr9ZAret7n+PUNjVDn5SMnrwg5eYXIyStEWUWV2c9CJgPcXV0RGR4CVUQooiJCERUeiiB/b9ZIiXqQ+voGLH3sF6ivbxDi77/5MnrHxUqUFRERERERERHRLWxuJCKHERsVgZ8sW4D3PvtWKMwcPHIC9fUNWL7kPri5ucLfpLmxK2cpNRgM2HPgMNZ+v+2OM2GevXgFmdfVWHzvNIwcnGRR8ahPQhw2bdtlHDc2NuF6thpxvWI6fUxHVVNbh5zcAuTkF7UU8/KLUFFV0+b2bq4uUIUHIyo85EYxLwQBfj6Qy1nMI+opCotKsOyJ54X3bIVCgdUfvInAAD8JMyMiIiIiIiIiIiKinkgul+FHi2ahsakZ5y/dWp1Qo9XiP6u+wvgxI3D/nOnw8/MVJnktK6/okvzq6xuw5rstOHrijNl9MpnMrEaaeV2NP7/zX9w3YxImjhne6TpcYu94bNu1R8hDnZuP6OjITh3P0RSXlmP/kVM4e+EySsra97M2GIC6hkZcupqNS1ezjXE3VxfEx0Ri7IiB6N8njhM9EnVzT//2VaRnXBFiP/nRUjy4YK5EGRERERERERERidjcSEQOJalPHFYunYdVKd9Br79VGDt17gKuq/OwbPF8BPiLzShl5V2zcmNZeQW+WLMBlzKzzO5zd3NFfUOjEKtvaMSnazbi1LkMLFswC16eHp06r5eXJyIjwpCbV2CMZVzO7DHNjdU1dTh4/CwOnziHkvLKDu3b0NiEy9fUuHzt1iyv7m6uGJLUGxNGDUJkWLCVsyUie6LT6bD8qRdQVFIqxP/8+19g7KihEmVFRERERERERETkAO4wwWO7OOyKYRZed6dZ+HhZ+vOyhEWpW7Czgz9HFQoFHn9oHlat2YTT6WJDyv5Dx5CecQWeHh5Cc2N5he2bG8+mZ2D12o2oqjafXHTUsMFYMHcGtuzcg31pR4X7bq7imJ1bgBWL5kChUJjsffefl6+vN0JDglBYVGKMZVy+gugoS5sbLXiuWPI8a8e+er0e5zOuIvXwCVy4Yl6D7qyGxiacy7iKcxlX4e/rjXEjB2Ps8IGdrld3HQlf1xa9pzjq7zzp3grvNJG07Vlw0ZY+Xja47s9SNmDVV2uF2KCkRLz1ym+tdxJJf17UMRY+SS3Z3eKniQUHkFnQxG9ofYXqDhyg87s66r+ZHPU9wZLnCTkWR31t9ViO+vOS8JemJb+7+PogIiIJsbmxNZb8A4u/2DvBQf9B67B/NDuylufKsIGJUCjk+Hj1d9DqdMZ7Kyqr8K8PP0N9Q5Owl61nKTUYDNh/6DjWb96OpqZms/snjh2FRfNm4uyFS/h67UbU1NYJ95+9eAX575XgmUcfQKC/b6dySOwdLzQ3Xsq8itmGqZY9Sy3+gM52DAYDrl7PReqRUziVfhk6nfVyrW9oxMHjZ3Hw+Fn0igrH+JGDMaR/ApROtv6VaXGlwypZkANw1A+CLWWD6371zffww/7DQmz2PRPx/M9+bHryzp/Eorx76M9aSpJ9sYGIuiV+tkBEREREREREFlA6OeHRpfPw5YbtOHzyvHBfeUUlGk3qkrasiVbX1GLdpu2trtYIAJPGjcKiebMgl8uxdMFcDB7YD1+s+c4sp6On09HY1IxHH7wPSmXHa2+JveNNmhszMX3qJMi62WcpGq0Wew4eR+rhEyivrLbpucorq/H9jlRs3n0AQ5L6YPaUsQgNDrDpOYmoa1y8fBU//dWfhJinhztS/vsW3NxcJcqKiIiIiIiIiMgcmxuJyCENTuqNnz6yGB98sc6scFdX3yCMbbVyo8FgQLY6D99t3YVLV661us3saZMwZ/pkyGQyDBnQDwm9orFm/RYcP31O2K60vBJvvP8Fnv7xUoSHBHU4lz4Jcdi1Z79xXF1di4LCIoSHhnT4WPbMYDDg1PlL2LLnEPILS+6+g4Wu5eTjWk4+1m51w4RRgzF9/MhOFVqJyP78sP8wXn7t30IsIiwEn/zrb5DLOSMfEREREREREREREUlPoZBjxYKZiI4IwfptqWjWaIz3ubqKjSmZ165b9dwtk43mIDXtKE6dvQDdbZPO3uTm6opF82dh9PDBQoNhn/he+P0vf4rvNu80W8Xx7MUreP+LtXhyxSI4OZmu4Hhnib3jsXf/IeO4orIKxSWlCAnueH3VXmXnFuCzbzehoKj0jts5KRQIDwtBVGQ4oiLDEREWAjc3VyidnCCTy6HVaNGs0aC0vAI56jzk5OYjJ7cAtXV1rR5Pp9Pj+NmLOH3hMuZOHYep40ZCoWC9hMhR1dc3YMmj/4N6k+/PvP/my+gdFytRVkREREREZBvyG7eejo8BkSNjhwYROazE+Bj8/tkf44u1W3HparYxblrIO5uegYrKKvj5+ljlvM3NzTh26hz2HzqGnNz8Vrfx8/XBssXz0a9PvBD39PDAj5cvxpBBSWarOFZV1+Ltj1bjuZ8sR0iQf4dyCgzwR4C/nzD7acblzG7V3FhdU4evv9+J0+mX77ids1IJVUQYVDcKeVGR4fDz9YbSyQkKhQJ6vR4arRZ1dQ3IzS+4UchruZmuqnlTbV0DtvxwCCfPXcLyBTMRqwqzxSUSURcpLCrBsieeh+G2FbUUCgVWf/AmAgP8JMyMiIiIiIiIiIiIiEgkk8kwcfRQJPXuhc/XbcOVLDUAwMXFRdhu5579+Ns//oOJySMxICkRnh7unTpfTU0tTp27iNRDR5FfUNTmdkmJCVi2eD58fbxbvd/VxQVLF8xFv8QEfPT5Gmhua8y8cPkaPlnzPX78wPwOTTgYEhwEXx9vVFbdWs0w43Jmt2hu1Gi12LL7IHamHoJeb2h1G3c3N4wZMQTDhwxARFgInJzu/pWfyPBQDO7fF0BLw2pFVTUuXspEatpRqPMKzLbXanXYsH0fTqdfxopFsxEWHGjZhRGRJJ7+7atIz7gixB5/eAkeXDBXooyIiIiIiIiIiNrG5kYicmgBfr545tEHsP/oaazfsgdNzc1mzY3XsrLx4l/ewoB+fTBscH9ER0YgMMBPmD30bmrr6pCTm48LGZk4dPwUGhoa29x27KhhWHDvDLiZ5HG7IQP6oVe0Cv/68DPk3VYUrKmtxz8//hrPPbEc/r6tFwJbI5PJkNg7HgcPHzPGMi5nYsqEse0+hr0yGAw4cTYDKRt3ma3KeZPSyQnDhwzAuNHDEa2KuGMRVKFQQKFQwNXFBQH+vhh0WzGvvKISh4+fxoHDx1FVXWO2b2FJOd74YDXuGTccc6YkcxVHIgek0+mw4qe/QlGJOOPxq7/7H4wbPUyirIiIiIiIiIiIiIiI7izQ3xfP/ngp9h89hfXbUs1qoo2NjcjJzcfnazYAAPz9fI0TgUZFhiM0OBDOzs5Q3miI02g1aGrSoLCoGNm3TQZ6e+Nga9parbEtA/r1wdOPr8C7H3+JxsYmY/zkuQz4enth0dx72v0Y3KyJHj520hjLuJyJiePGtPsY9uhuqzVGRYZjYvJIDBvcH87Ozp0+j0wmg7+vD8aOGobkkUORrc5DatpRHD99HlqtVtj2em4B/vqvTzB36jjcM35kh5pQiUhan6/5Dqu+WivEBib1wT9e/Z1EGRERERERERER3Rm7MojI4clkMkwYNQRJvWPxxdqtuHDhgnB/U1MT9Ho9zpy/iDPnLwIA3NxcERURhqjICISHhcDVxRlKpRJymQwarRYajQbFpWXIyS1AjjoP5ZVVd82jrdUa2+Lj7YVfPPVj/Pu/XyArW22Ml1dW491PvsGvf/YjKF3a/zad2DtOaG4sLilDeXkF/P0ddxWymto6rP6u7dUagwL9MSF5JEYPHwwP987NPnuTTCZDgL8f5kyfjJlTJ+BsegZS047iUmaWsJ3BYMDO/cdwNuMqHl44EzGRXMWRyJH8+a3/YHfqISE2a+oEvPDzRyXKiIiIiIiIiIiIiIiofeTyllUc/f18cOjwYeG+xkZxctbyikqUV1Ti9DmxdtpZSicnDB86EHOnT4afr0+H9o3vFYOnH38Y77z/KZqam43xHw4ew4C+CeiT0Kvdx+qTECc0NxYUFqGqqho+bawgae+On72AT9dshE6nN7tPFRGGpffPQa+YKKufVyaTISYqEjFRkVhw70xs270Pe/YfhsFwa9XIm6s4Xs3Ow2MPzufEr0QO4OLlq3jqhT8KMU8Pd6R89Bbc3NqeoJuIiIiIiIiISEr85JGIuo0AP188vHgOdvywT4ibFvIAoKGhEZcys8wa1zrD388X48cMx4TkkXdcrbE17u5u+PnjK/D2fz5BTm6+MZ5fVILNuw/gvtntn6k0IjwMnp4eqK2tM8YyrlxF8qjhHcrJXpSUVeCdj9egrMK8sdTTwx1L7puDoYOSbDJLqEKhwJCBSRgyMAm5+YX4Ys0G4ecDAEUl5XjrwxSsXDIHg5MSrJ4DEVnfngOH8af/+5cQiwgLwaf//jtnHCYiIiIiIiIiIiIih6DT6bBp5wG4uLgI8aampjb2sExQoD8mjBmB0SOGWDTZaGy0Ck+sfBDvfvQFtDqdMf7F2s34/S+egKtL+1YkjI6KhJubKxoabtWAL125ipHDh3Q6N6kcOHoKqzdsw239hABaapWz7pmIGVPGQ6FQ2DwPTw93LJo3C0MG9MPnKRtQXFom3H8uIxP//vQbPLliAVxNnndEZD/q6xuw9LH/QX19gxB//82X0Se+/U3kRERERERERERdjd/iJqJuw2AwYPX67VAqxcKXLQp5MpkMSYkJeOrHy/Dyb/8HM6ZM6HBj401urq74+eMrEBocJMR3ph5BVk5eu48jl8vNZjXNuJzZqZykll9Ygjc++KrVxsahA5Pw4gtPY/iQAV3SjBQZHooXnn4c82bdAyeT4qFWp8NHX2/E4ZPpNs+DiCxTVFyKZU+8IMw4rFAosPqDNxEY4Lgr3BIRERERERERERFRz7J93xGoC4rhalKbdHVxRlJiApROls9x7axUYmBSIn7++Ar876+ewdSJYy1qbLwpMSEO98+dIcTKKqqwYevudh9DLpejd7zj10T3HTqBr9abNzaqIsLw62efwOxpk7qksfF2cbHR+N1zT2HqhGTIZDLhvsvXcvDPVWvQ2NTcxt5EJLVnfvdnnL94RYg9tmIxHlwwV6KMiIiIiIiIiIjahys3ElG3ceTUeZy/dLXVWUoXzpuJi5cykZObj9q6+k4dX+nkhMiIUCTExWLsyGEICvS3RtoAAE8PDzy2Ygn+9o//GGcqNRgM+PybjfjtM49BqWzf23ViQjxOnDpnHKvz8lFXVw8PD8uLjV2loKgUb330NepMZhP09HDHAwvmYuig/l2ek0KhwMypEzCwXx98lrJeWMXRYDDg83XbYIABY4Z2fW5EdHc6nQ4rfvorFBaXCPFXfvssxo0eJlFWREREREREREREREQdk1tYjK17DwGAWXNjXV09fvbYCuh0OhQUlUCdm4+c3Hzk5OUjN68QGq221WM6K5VQRYRBFRmOaFU4oiLCERIcaLNJRieOHYlTZ9ORmZVtjKUeOoEhA/qiT1xMu46R2DseZ85dMI6v56jR0NAANzc3a6drE0dPnUfK9zvM4hOTR2LR/Fld3tR4O2dnZyycNxMD+vXB+5+sRkPjrRUys3Ly8cGX6/DUw4us0kRLRNZhMBjw8Rff4uMvvxXiA/r1xtt//r1EWRERERERUZeRyVpuPR0fAyKHxk8biahbqKyuwTcbdwEwL+QBwMC+vTF1QjIMBgMqKqtaCnk3bhWVVdBotdBotDAYDFA6OcFJ6QQPdzeowsMQpYpAVGQ4QoMDbVpICg8Lwezpk/D9bTOTFhaXYvOuVNw3a0q7jhEbEwVnZyWamzUAWj7EvnTlKoYOHmCTnK2trKIS//w4xayxMTI8FD97bAV8vL0kyqxFeFgIXnj6caSs34wDh48L9325fgfcXF0wuF+CRNkRUWtqa+vwx9f+hV370oT4zCnj8aunH5MoKyIiIiIiIiIiIiKijjEYDPhq/Q7odHoAMJvwtbyiAgaDAQqFApHhoYgMD8WYkUMBAHq9Ho1NTdBoWmqiMhng5OQEpdIJri4uNmtkbI1cLsfypffhz2+8C41GY4x/+e0m/O/zT7WrHhsXGw0nJwW02pZJY/V6Pa5czcLA/v1slre1ZGRm4bNvvjeLz7pnIubOmGK2YqJUesfH4n+eWol/fvAZauvqjPGMzGx89s1m/PiBeXaTK1FPlZWtxpffbsQXa77D5avXhfs83N2x5r//gJub+fdniIiIiIiIiIjsDZsbiahbWLdlDxoamwC03txYVl6BoKBAyGQy+Pv5wt/PF4MH2F9xa9qkcTh97qKwMuDOfYcwaugAhIUE3XV/JycnxPeKwYWMK8bYpUzHaG5sbGrGPz9eg8rqGiEeFxOFpx5dBnc7mWlVoVDgwYX3ws3VFTv3HjDGDQYDVqVsxi8eX4qYyDAJMyQirVaLXfsO4YtvvsOGrbtRb9IwHREWgk///fcu/bIGEREREREREREREZElrmbn4XpugXFsWhPV6XSoqqqGr6+P2b5yubyl1mYf5TYEBwbgvtnT8M13W4yx0vJKnEm/hKED717DdXZ2RlxsDC5duWqMZVzOtPvmxobGRnz2zUbo9QYhPmf6ZMyZPlmirNqmigjDL55aiTff/Rh19fXG+IlzGRjYNwEjBtv3403UHVVUVmHNhq348tvvceDwiTa3+88bf0Kf+F5dmBkRERERERERUefxG91E5PAqq2pw8txF41ihUJjNVFpWXt7VaXWKQqHAw0vvh9NtM5IaDAbsTTt+h71Eib3jhfHVrGw0NzdbLUdb2bBtL4pLxZ9TbHQkfvb4CrtpbLxJJpPh/rnTMW3SOCGu1enw2dpt0Gi0EmVG1HMZDAacOHMev/jDX6EaOAmzH3gcX63dZNbYqFAosPqDNxEU6C9RpkREREREREREREREHZd65JQwjggPNdvGUWqiADBx7EhEqyKE2L5DbTfqmDKtiWZeuw6t1r5rdGs37UJllTjR65QJYzB72iRpEmqHsNBg/PzxFXA1qb+v2bgTVTW1EmVF1LM0NTVj/eadWPijnyOs31g89fz/3rGx8fGHl2DZonu7MEMiIiIiIiIiIsuwuZGIHN6Bo6eF2S1dnJ0RHBQobFNeUdnFWXVeeFgIpkxMFmJHT54zrkx5NwlxscJqZFqtFlezsq2ao7VdunodqYdPCrHwsBD87FHzQpk9uW/ONIwbPVyIFZWUY/MPaRJlRNTzZKvz8Je3/oP+4+ZixD2L8Pb7n6KopLTN7f/+v89j3OhhXZghERERERERERERmTEYet7NsgfMwpuj4jXfvFXV1OJU+mVhy3smjjWb8LWouKSL8rScXC7HlPFjhNiVa9koKGrfNfSO7wWZTGYcNzc3I+t6jlVzvLv2PyfTL2Ui7fgZYe/EhF5YMHeGcB32KFoVgRVL7xNidQ2NWL1hBwwWv791hEy6m8yCm0Oz5HEjSxgMBhw8cgJP/vIlhCeNw8If/RzrN+9Ec7OmzX3iYqLw1qu/w3uv/bEzZ7TgZgkpX5sSndfS9wVJ34+kfMwkej+SyS27Sfa7S0KW/qyluvXU6+6JN5KAzIJbT2TJ4yXhY2bQt34jIiJqJzY3WpvURTFHvDksSz7Yc+TrllArzx+dVosDx04Lm40cNggB/n5CLC+/oAsTtdzE5JFCg2JTczOOnjrXrn3d3NwQExUpxDIuZ3Y8iS76EKCxqRlfrN0qxJyVSjzxowfh7m5fKzaakslkWHr/HERFhgvxXQeOI0ud39XZWHCjnsPS31328XuvsqoaH33+DSbPX4HYoVPxh7/8AxcvX21ze4VCgTnTJmJryod47qmVVs2FiKR8X3HU9zP7eC8lOyf1v9Ol+je+1Ln3uM81JMSfFxERERERUYekHT8Hne7WlwKVSiWSRw5DlOnKh/sdawLOwQP7wdPDQ4il3mE1stt5eLhDZVKjy7jSiZpoF2hobMSX67YIMVcXFyxbfJ9QE7ZnQwYmYdjg/kLs7MUrOH7mokQZEXVPlzOz8NJf30b88Hswfs5D+ODTFFRUVrW5vb+fL55c+SAOblmNy0e349knHnaY9xUiIiIiIrISGaRvHreLm9Q/CCKyBD/NICKHdubCFVTX1Amx8WNGoHdCnBDbtWdfV6ZlMT9fHwzo10eIpR46gfbOfJnYO14YX868Br3ePmdB2bBtL8oqxA/j58+ZhqBAf4ky6hiFQoGHl94PJ4XCGDMYDPh83XZoNFoJMyPqXpqbm/H9th+w5MfPIixpHH7y3IvYl3bsjvuMGDIA//jz75B3LhUbv3ofM6aM76JsiYiIiIiIiIiIiIisQ6fT48CxM0Js+JABcHd3w/Spk4T45u07uzAzyymdnDB21FAhduTEWTQ2Nbdr/8TeYk340pWr7a6ndqUNW/egsqpGiC24dwYC/H2lSaiTlt4/x6wZdc3Gnairb5AoI6LuoaS0DP/68HOMnr4YiaNn4tU33kVWdm6b2zs7K7Hw3hlY/9m/kZ++H+++9keMGTEE9r4KLBERERERERFRW9jcSEQObd/hU8I4LjYKkeGhmHnPFCG+Y/deNDe3rwhmLyYmjxTGBUUluJKV0659+5g0dzY0NiI7p+0Pv6WSlZOP1MMnhVh8r2iza7d34WEhmD19khArKinHjtSj0iRE1E0YDAYcPn4aP/vVnxAxYALuW/FTfLtxO5ru8KWGmKgI/P65p3AhbQuO7PgGz/zkYQQHBXRh1kRERERERERERERE1pOlzkeFSWPczVranBnThPihI8dRVlbeZblZw7jRI4SGnMamZqRntG8FxsQEccLX2to65OblWzU/S9XU1uHQ8bNCLDGhF8aOGiZRRp3n6eGBBxfOFWJ1DY1IM7k+Irq7hoZGrNmwBfMe+gkiksbhmd+8jKMn7/xamjBmBN5/8xUUXkzDN6vewfzZ98DZ2bmLMiYiIiIiIiIish0nqRMgIuqs2voGs2a/CTcKebNn3CPEa2pqsf/gYUydPKHL8rNU7/hYBAcFoLikzBg7fT4DvXtF33VfHx9vhIUGo6CwyBjLuJKJ2Jgom+TaWT8cFFddc1YqsWLJ/ZDLHa/3ftqkcTh97iJycm8VTFOPnMb0CSOhVPLXLVFHXM3KwRfffI8vv/kemVnZd93e18cbi+fPxPJF8zB21FCHfA8hIiIiIiIiIiIiImrN9dwCYRweFoKoyHAAwOSJ4+Du7o76+noAgF6vx7adP2DZA4u6PM/OCvD3RZ/4WGRcuWaMZefmY9igfnfd19/fD8FBgSguKTXGMi5nQhUZYZNcO+PQ8bPQ6nTGsZNCgWWL73PYFdaGDEzCoP59ceb8RWMs9cgpTB03EnK5Y14TUVfR6/VITTuGz9dswNrvt6G6pvau+/SJj8XyJfOxbNG9iImK7IIsiYiIiIiIiIi6Hr/5TUQOKyevUBgrnZwweEBLkSskJBgjhg0R7t+0dUeX5WYNcrkcI4cMFGI5JsXLO0nsLc5UmnH5KgwGg1Vys4aq6lqcOn9JiM2eNglBgf4SZWQZhUKWSOy/AAEAAElEQVSB5UvuE2K19Q04lX5ZmoSIHExZeQXe+/grjJ31ABJGTMOf/u+fd2xsVCqVuG/2Pfh21TvIP78f77/xMsaPGc7GRiIiIiIiIiIiIiLqVnLyioRx7G3NLa6urpg6abxwv6PVRAEgNloljHPyLKmJZtpNTVSv12P/kZNCbOjg/gjw95UmISuZNmmsMC6rqMLF25pTiUh0IeMKfvvy64gdPAlT5i/Hqi+/vWNjY3BQAJ554mEc3fktLhzait8/9xQbG4mIiIiIiIioW+NSUkTksEybGyPCQ6F0uvW2NnfWdBw7cco43rx9J976v1e7LD9riFaJs4rm5hdBp9NDobh7805i73jsST1oHFdVV6OouAShIcFWz7MzDh47A71ebxwrlUqMGz1cwowsFxkeij7xsbiUmWWMpR45jZGD7z6zLFFP1NjYhE079uDLb77Hll2p0Gg0d90necQQLF8yH0vmz4S/n6/tkyQiIiIiIiIiIiIiklBOvtjceHPVxpvmzpqOjVu2G8fbdv0ArVYLJyfH+TqIyuSa1HmFMBgM7VrdMMpklcay8gqUlpUjKDDAqjl2RvrlayirqBJiE5NHSpSN9cRGqxAZHorc/Fv1+n1HTiGpT5yEWRHZl8KiEqxetwlfrNmAU2cv3HV7NzdX3DfrHixbPA/TJiVDqVR2QZZERERERERERPbBcT7NJiIyoTZpbow2KXrNmTkN//vq343jK5nXcPnKVfROcJyiimlxslmjQVFJKcJD796g6OnhAWelM5o1zcZYxuVMu2hu1On0OHD0tBAbMWQA3N3dpEnIiiYkjxSaG7PUBVDnF0EVHiJhVkT2Q6/X48DhE/jim+/xzXdbUVVdc9d94mOjsXzJPCxbeC/iYqO6IEsiIiIiIiIiIiIiIuk1NDahuLRCiJnWD+fMnCaMKyurkHb4KCaMS7Z5ftZiek0NjU0oKatAcKB/m/vodDocOX4Se/cfNrvPXpobUw+dEMaqiLBusfqaTCbDhOSR+Orb742x9EtXUVZRhQA/HwkzI5JWXV091m/eiS+/+Q479x4UJntujUwmw5TxY7B8yXzcP2cavL08uihTIiIiIiLqXuQ3bj0dHwMiR8bmRiJyWNkmzY2mRa8hgwYgLDQEBYW3ZjPdvG2nQzU3enl5ws/XBxWVt2b0zMkruGNzo16vx4lTZ7F73wGhsREAqmtqbZZrR5zLuIJKk4amCd1gllIAGJiUCB9vL6FhK/XIGSy7f7qEWRFJL+PKVXy+5nt89e1GZKvz7rp9gL8vlt4/ByuWzMfIoQNx97mZiYiIiIiIiIiIiIi6F3VBsTBWKBQIDxMn1IwID8Pggf1x+ux5Y2zT1p0O1dzo5+MNL08P1NTWGWM5uQVtNjdm5+Ri8/ZdKC4pM7tPoVAgwN/PZrm2V21dPS5cvirEJiSPbNdqlI5gxJCBWLdpOxobmwAABgNw7MwFzJw0RuLMiLqWTqfD7tQ0fLnmO6zbvAN1dfV33WdAvz5YvmQ+HlwwF5ERYbfuMNy5GZKIiIiIiIiIqLticyMROaTa+gaUV1YLMZVJc6NcLsfsGffgv59+aYxt2roDv3j6yS7J0VqiIsPF5sbcQoweNqjVbfPyC7B5+27kFxS1er+9zAR64myGMI6JijRrTnVUCoUC40YPx+Yde4yxU+mX8eD8aZDLu0exkqi9ikvKsHrdJnyx5jucOJN+1+1dXJwxf+ZULFs8DzOnjodSqbx1p8Fgw0yJiIiIiIiIiIiIiOxPcUm5MA4LCYLSyfxrHnNnTReaGzdv34n/+/P/2jw/a5HJZFBFhOHCpUxjrLjUvHGxtrYOO/ek4sy5C60ex83NFfNmz0BwUKDNcm2vnLxCobShdHLC8MEDpEvIylxcnDF88AAcOHzcGLuuLpAwI6KuUVdXj7yCIuTmF2LLrr1Y/e1GFBQV33W/8NAQPLhoLlYsuQ8DkxK7IFMiIiIiIiIiIsfB5kYickhl5ZXCWC6XIywkyGy7ubOmC82NqQcPobq6Bt7eXrZO0Woiw0Nx5vxF47i0vMJsm4aGBuzeewAnTp+DoZUGIBdnF9wzaRwG9u9n01zbKztXLGyNHj5YmkRsZPTwIUJzY0NjE0rKKxDSxuyyRI7OYDCgrLwC+YXFyCsoQk5uAb7fths79hyETqe76/6Txo7E8iXzsfDeGfBxoPdnIiIiIiIiIiIiIiJbatZohbGnh3ur282ZOQ2v/v1N4/jCxUvIup6N2Jhom+ZnTR4m13b7tev1ehw/eQY/pB40rhRoaujgAbhn0ni4u7f+GHW1nDyxHhoZEQoXF2eJsrGNXjFRQnNjTn6hhNkQWUar1aKwqMRY78wrKER+YTHyC4puxFrGVdU17T6mh4c7Fs6dgeVL5mPy+NFQKBQ2vAIiIiIiIiIiIsfF5kYickimhTxXF5dWPwi+Z/IEODs7o7m5GUDLB9I7du/BovvndUme1uDm5iqMNbddu8FgwOmz6di5JxX19Q2t7j+gXyKmT5kILy9Pm+bZXvUNjSg1aU6NjVZJk4yN+Pv5wNvLE9U1tcaYOq+IzY3kkOrrG5BnLNrd+m9BYTHyCouMseZmTYeO269PPJYvmY+HFs7tNiu3EhERERERERERERFZU7NWrIk6tbJqIwCMGDYEQYGBKCktNcY2b9uJnz/5mE3zsybTFSlv1kRz8/KxeftuFBS2vjJaWGgI5syYisgI+6o15OSKjX5RdpafNZjWdyqralBTWwcvTw+JMiIyZzAYUFFZdaOmeaO2WXCz3tkyzissQlFxaasTSXeUXC7H9MnjsHzJfMyfdY9Z4zYREREREREREZljcyMROSSNSSFPqWz97czT0xOTxidjx+69xtjmbTsdqrnRWakUxjevvai4BJu370aOOq/V/QID/DFn+hTExkTZPMeOyMkTC3lOCkWrq246MplMBlVEGNIzrhhjOflFGD6or4RZEYm0Wi2KSsqQX1CEvNsaF/NNGhkrq6qtds6Q4EA8uGAuli+ehyED+0Emk1nt2ERERERERERERERE3Y1epxPGba36pVAoMGv6FHz21RpjbNPWHQ7V3Gh6bXX19di4dQdOnDrX6vauri6YMmEshg8dDLlc3hUpdohpTbQ7TvQYGhwIZ6USzZpbE2Dm5BUiqU+chFlRZ2Reu45VX32L/IIihIYEISQoEKHBQQgJvvVfP18fu6vtNTQ0Ir+w6K6rLba14qs1DRvUH8uWzMMD989FaDf7/gMRERERERERka3ZR3OjQd9yE0j4gZh9fRbXRSS8aCvMfCYJiz+0leq6LcvbkpnqZJZe822PuV4vvmfcqWA1d9Z0k+bGXdDr9XZZ5GqNaZ5NTc3YtnMPjp44bfY4AC2NnhPHjcGYkcPaLHC2i41em6aFvPCwkDZnmXVkUZHhJs2Nrc8maz0O+l4qpXY+x1ev3YTVazeiWaNBaHAggoMCERoc2FLUC7nx3+BA+Pv5dtH7yp3zNhgMqKyqvq2IZ7La4o1YUUlZq+8h1ubu7ob7Z9+D5YvnYeqEMeLrvUPvMxY8xx31bw2L/z5z0Ou25G8sh/1Zk2Ph84zojnrie3FPvGaHZunPy5K/0fhcISIi6vEMBvv6+5E10a5lyc/eYeuSFp5a0iYaSxLv/L6mTxPTWp/WZALY282dNV1obtyTehC1tbXw9PTsdD5dSXujkdNgMKC0tBS7f9iLuBhVq9sOGtAP0yZPgKenB+zxzay2rh7llVVCLEoVIVE2tiOXy6GKCMPV6znGWE5+EZIS421zQnv6HdoRlr6X2fi6GxubMGvJSmRey77jdkql0qQ+2tL02FojpI+3l0WXrdPpUFxSJq62WGiy2mJBESpMXmddydvLEzFRkZg9bRKWL56HfokJt35Wjvpcpa4jVT3Uzt+P7siix8ySE1v6XTcLvkNi9n3aDp3Ygn0t5aDvgXbWwE9kX/j6ILJ7Mhl/lwF8DIgcXPfrJiGiHkFp0gx3p0LenJnT8MzzvzOOS0pLcezEKYwaMcxm+VmTRtNybQaDAeXl5cjNyYamoabVbfv2icfMeybDx8e7K1PskJ4wSykARJsUKNX5RdDrDZDL+cezIzly4gyWP/nLdjV2Ozk5ITjQXyjq3Sroic2QnZ3ZtLGx6cbso0XIKyi+tcLizYLejZlJGxoaO3O5FpPJZAgODEBEWDBioyIxb9ZU3D/7nhtfLiAiIiIiIiIiIiIioo7oSE10+tTJcHJyMm7T3NyM3Xv3Y/7cWTbN0Vo0Gg3q6+tx/fp11NXVtdrYGBQYgDkzpiImuvWmR3tRWFImjJ2cnBAaHChRNralihSbGwuLy+6wNdmjisqquzY2Ai2v0dz8AuTmF9x1WxcXZ5Omx1u105uxpqZm48qKxlUXC1r+v7C4BDqTlWu7ilKpRHhoMCLCQhAeGozwsBDj/0eEhbbEQoNZ/yQiIiIiIiIisiI2NxKRQ3JyEmcpvdkA2JpesTHom9gbFzMuG2Obt+10oOZGDZqampCVlYWamhoE+vuZbePn64PZ06cgIb6XBBl2TElZhTBWRYRJlIltRZpcV2NTM2rq6uHjxSKHI7l2Xd3uFWu1Wi3yC1saDnHuztu2zGwaIBTybjZCBgX4o7qmtqWIV3hz9tFi5BcWoay80vKL6iQvT4/bCnchxsLd7bHQ4EAolUrJciQiIiIiIiIiIiIi6k5cXZyFcWVV6xOgAoCPjzfGJ4/GntQDxtjmbTsdprkxP78QFy9ehF7fskqR022rVjo7KzFp/BiMGj7UbDVLe9TcrBHGHu5uDpF3Z3iZNHg1aTRtbEn2KsDfF36+PlZdBbGpqRk5ufnIyc232jGtISjQHxFhIYgIC0WYUOcMNdY7A/x9IZdbsNIaERERERERERF1GJsbicghubu6CuPGpibU1tXD08O91e3nzJgmNDdu2roDL7/4G5vmaC3FpWXIyMhAc3MzAEB5W2Onk5MC48aMxNjRIxymoahZY1rMa/1n5ug83NzMYndqwiX7NKh/ok2O2zKzaSFy8wvvvrGNOTk5iY2KYTf+P1RsZPTy8gTa2ehJRERERERERERERESWCw8RV/srLi1DY2MTXF1dWt1+zsxpZs2NBoMBMpnMpnlaSq/X49SZc8bGRuBW01xS396YMXUSvL29pEqvw0xrgs4OUsftDKWTeG1a1kMdjrOzMxbNm4UPP/vaGJPJZOgT3wuFxSWorKqWMLv28fBwR0RoyG2rLYYa654tsRCEhQTB2dn57gcD66FERERERERkH9LT05GSkoLz589DrVajurrl3+je3t7o378/Zs6ciaVLl0qcZfup1Wp8/fXXOHToEKqrq6FWqwG0XI9KpUL//v2RnJyMmTNnSpwpSYHNjUTkkIID/eDkpIBWqzPG1Hn56Ns7vtXt586ahtff/rdxfOrMOeTlFyAi3P5XDTx15pyxsRFAS4MRgPheMZg9fQr8W1nJ0Z6ZFvOUyu75q6i169JqWcxzNP36xGNQ/0ScOZ9hjAUF+qN/Ym8UFpegqKQM5RWV0iV4F4EBfrc1KIaYrbQYERaCwAA/zj5KRERERERERERERGSHwkMCIZfLjU1/BoMB6rwCJMTFtLr93FnT8fzv/tc4zi8oxOkz5zBk8MCuSLfTSkrLUVJaKsRioiKwaP4cxPWKkSYpC+h0OmHcXVdtBACFk3htGpNrJ8fw0KJ5QnOjwWDAN6v+hf79+qCxsQnFpaUoKi5FYfHN/5agqKQUhUU3/ltcgqLiUlTX1Fo1L4VCgbCQoFZWWwwRap6O1PxMREREREREdDdpaWl4/fXXkZ6eDgBITk7GrFmzoFKpUFVVhfT0dGzduhVpaWl46aWXsHTpUjz//PPw9vaWOPPWpaen48UXXzRej6nq6mqkp6cbmzkB2P01kfV1z44SIur2FAoFIkODcT23wBjLyW27uTF59Ej4+Hij6rZZBbds34XHV66wea6W0Ol0yLicKcTCQoKwdOE8JPaOt/tZVluju23GVQBQKLpnU5VcLodMJoPhtpXutCzmOaQHF9wrNDdqNFpsSfkILi4tM3s2NzejuLT8tuJdKYpKSlFUUoaiopYGyJuNkNaa2dTNzdU4+2hEWPBthbxbTYthIcHGHImIiIiIiIiIiIiIyPEolU4IDwlAbkGJMZaTl99mc2PvhDjE9YrB1WvXjbFN23bafXPjuQsZaGhoMI5dnJ2xbMn9iIwIlzCrzjNtZtTquu8EqKaTuyq7cSNndzYheSQiwkKRV1BojK1etxF/7tcHrq4uiIqMQFRkxF2P09DQ2FInLS5FYXHxrYZIk0bI4pJyKJVOxrqmsVkx7LbVF0NDEBTo362bg4mIiIiIiKxCJmu59XTd5DF45plnsH37dgBAUlISXnnlFSQlJZlt9/LLL+PDDz/E66+/jpSUFGzduhWffPJJq9tK6aWXXjI2LN5clTE5ORkqlQpAy2qON5sa09LSjPulpKQgJSUFzz//PB5//HFJcqeuxeZGInJYqohQk+bGgja3VSqVmDltClK+3WCMbd620+6bGwuLSlBWVibEZt4zCX37JEiUkeWcTIt52u7Z8KfT6YTGRgBQOvHXriN6YMEc/Obl14zjyqpqbNudivmz7wEAODs7IzI8FJHhoXc9VsvMpjeaHYvLxGbIGzOdlpSVw8vTo6VB0WT20Zb/D4KPt5dDNjcTEREREREREREREVHHRIWHiM2NufltbiuTyTB35nS8/e4HxtimrTvw4m9+adMcLXXsxGlhHBToj4jwMGmSsQKlUqwJajQ9qLlRyXqoI5LL5Xhw4b14/V8fGmNfffs9Xv39LztUk3Rzc0VMVCRioiIBGO66fVtM6+xEREREREREPcHKlSuNDX7JyclYtWrVHbd//PHHkZycjAULFqC6uhoLFizAqlWrkJyc3BXp3tXNRk2VSoW333671cZLlUoFlUqFmTNnIj09Hc8++yzUarXx/tdffx1paWl3fSzI8fFTRSJyWNERodh/2zhHnXfH7efMmCY0N+78YR8aGxvh6upqmwSt4MTpc9BoNMaxq4sLhgzoJ2FGluspxbzWrsuJzY0OKSoyHONHD8f+w8eNsdVrNxqbGzuiZWbTcERFWjLTMIt5REREREREREREREQ9hSo8BDhx3ji+ei0ber0ecrm81e3nzJwmNDceO3EKRUXFCAkJtnmunXXyzDlhnJTY26EneXRxVgrjurp66HS6brkCXXVNnTB2dnaWKBOy1EOL5gnNjddzcnHo6EkkjxomYVZEREREREREPcMzzzxjbGxMSkpqdzNfUlIS3n77bTz77LMAWhokd+3aZVwZUSo3GxtnzJiBd955p137JCUlYdeuXViwYAHS09ON8bS0NKxcuZINjt1c6592ExE5gKgIcZW0sopKlJaVt7n9rOlThSJYfX099u1Pa3N7e3DwyDFhHBEWgoAAf4mysQ5XFxdhXFlVJVEmtlVRVW0Wc3VRtrIlOYIHF94rjL/f/gOqa2olyoaIiIiIiIiIiIiIiHqK2ChxwsTyyipcvHy1ze0njBsDT08P49hgMGDrjt02y89Sl69mITtHLcRGDh8sTTJWEhocKIy1Oh3yC4slysa2TFcSDQsOkCgTstTgAf2QmBAnxL5a+71E2RARERERERH1HCkpKdi+fbtx/Pbbb3do/5kzZ2LGjBnG8cqVK62WW2fcvJ7k5OR2Nzbebt26dfD29hZiaWlpeOmll6yVItkhNjcSkcMKCw6Au5u46uL+Q8fb2BoIDAzAmFHDhdimbTtskps11NbV49iJ00Js8EDz5ZgdTURokDDOyS2QKBPbMi3k+Xh5wsPdTaJsyFKL588UVt5sbGzChi07JcyIiIiIiIiIiIiIiIh6gsjQILP6Wmra0Ta3d3FxwfSpk4TY5u32W9NYv3ErDAaDcezm6oppE8dKmJHlPNzdEODvK8RMa4fdgU6nQ25+oRAznaCYHIdMJsNDi+YJsTUbtkCj0UiUEREREREREVHP8Prrrxv/f8aMGZ1adfGJJ54w/r9arcaHH35oldw646WXXoK3t3eHmzRv19q+KSkpwoqO1L2wuZGIHJZCocDoof2FWNrRk3f8cH3OzGnCeNPWnUKxzJ7s2ncANTU1xrFMJsPcGZMlzMg6VCYFrezcPIkysa0ck+uKigiRKBOyhgB/P8ycMl6IrV67SaJsiIiIiIiIiIiIiIiop5DJZJgwarAQO3/xMsrKK9rcZ84MsSa6fdceNDc32yI9i9Q3NCD14BEhNnLYQLiZTHDriEyb/Lpjc2NhcalZbZ7NjY7twYX3CuOS0jLs3pcmUTZERERERERE3d9rr72G6upq4/iBBx7o1HGSkpKEpsgPPvjA4tw642ZT5ZIlS8xWX+yI5ORkJCWZLwr14osvdvqYZN/Y3EhEDm38yMHCuK6+HifPtt2RP3fmdGF8PTsHFzMu2yI1i+j1emzaukuIRUWGoW/veIkysp6ocLGgVVxShobGRomysR3TAmVUOJsbHZ1pMW/XvjQUFZdKlA0REREREREREREREfUUwwcmwtXF2Tg2GAw4cPh4m9tPGDdGGNfU1OLEqTM2y6+zDh09hfLycuNYJpNh1j2TpEvIikyb/NTdsLnRtB7q7+sNTw93ibIha4jvFYNRwwYLsS+//U6aZIiIiIiIiKid5LwZb45nzZo1wjg5ObnTx5oxY4bx/6urq7Ft27ZOH6uzbjZV3r6SZGf95Cc/MYulp6dz9cZuyjFfwUREN4QEBSAxPlqIpaYdbXP7xD4JcHcXCyr7DtjfTIMXL1/F9ewcITZ53GgoFAqJMrKeiNAgyOXirx91XoFE2diGXq9Hbl6hEOPKjY5v3swpcHd3M451Oh3WfLdFwoyIiIiIiIiIiIiILGAwSHeT7qIlvFmStiP/rCx4zCzK28KbnT2/XV2cMWqIOFP5gcPHUVtX1+r2r/3j32Yxe6szNjc347stO6DVao2x0KAADBuY1LU/axuJiggTxrkFRWhsbLLZ+aRw7bpYz265ZtldbhaQySy7WXZyC26Wnrprr/mhRfOE8frNO1Bf32D5dXSp7vP+7xAke10CFr02Lcrb0ltP5KiPmYR5y+TS3SxLnLcecyMiIiJLpaWlCas2trZSYUcMGDBAGKekpFh0vI5KT09HdXU1VCoV3n//fYubK9tq9Nyyhd/d7o7so7mx1Q999BLeLPywyiELS9b40N8Rb5Y8ZI76s3Zkrf8cJ4waImyVlZ2LC5eutHqEv73xDurr64WYt5eXLZLtNL1ej++37kJNTY0x5uXpgfGjh3eL54pS6YTwkEAhlnn1ujTJ2EhObgGampuFmOquKzdK+H5G7eLh4Y77Zt0jxFav3dTJo1nws5b094+EN/7e7FoWf0GAH4ATERGRnZL68zv+vUpERERERJ00YeQgYVxX34A1682/yLNj1x588PFnQmzShLEYMWyI2bZS+m7rLmRdzxZiQwf1h6+vj0QZWVd0RChktzXuaLVaHD15VsKMrKuxsQnHT58TYjGqcImyIWtact8cYbLiurp6bNy2W8KMiIiIiIiIiLqnr7/+Whj379/fouOZNkeaNk/aWlpay4JTarUaH330EZ599lncc889nc7B29sbKpXKLH7o0CGL8iT7ZB/NjUREFhiQGA9fb08h9sWa79DQ0CjETp85h1f+9oYQ69e3DxbeN9fmOXbEngOHcfb8Bej1emMsNioCCXGxEmZlXb2iI4XxwaMnhet1dAePHBfGAb7e8PHykCgbsqYHF4rvF4ePnzablZaIiIiIiIiIiIiIiMjaQoMDMLhfghA7fvocTp27YBxXVVXj0Z/+j7CNh4c7Pn7vbaHRTmqZ165j74EjqKqqMsb8fLwxbvQwCbOyLnd3NwxIjBdiqYeOwtBNJrk5evIMmppuTfYqk8kwfFA/CTMiawkNCcLUCeLKCF+t/V6ibIiIiIiIiIi6L9MmvdYa+Tqitf23bt1q0TE7orKy0iymVqvx/vvvd/qYrV2TWq3u9PHIfrG5kYgcnkIhx+wp4ofrlVXVWLdpu3Hc3NyMH/3kaWi12tv2U+DTD/4FV1fXLsv1bopKSvH9ll3CL3d3N1eMGTEEbm5u0iVmZaOHisteV1RW4fzFyxJlY131DQ1ms66OHmrZTBpkP6ZPHocAf18htnrdZmmSISIiIiIiIiIiIiKiHmXJvVPg7ibWNr9euxG1dXUAgOd+8yJy8/KF+1//y58QGxPdZTneTXNzMz5fswGNjY1oaGgAAMjlcgzs1xuJCXESZ2ddE8aIzZr5BUW42g0mzTQYDEhNOyrEBvZNgL+vt0QZkbU9tGieMN66ax90Op1E2RARERERERF1P2q12mxFQ0ubG4GW1Q5vl56ebvEx26umpqbV+IULF1qNt4eXl5dZrCtXo6Suw+ZGIuoWxo4YhD5xYlHu4JETuHDpCgDglb+9gbPnxV/Ov33+WQwfOrirUrwrvV6Pz1PWo1mjEWYpHdivN/r1SbjDno4nRhWG6MgwIbbv4BGJsrGuw8dOQaPRGMdyuRxjRwy4wx7kSJRKJRbPny3EdvywX6JsiIiIiIiIiIiIiIioJ/Hx8sTiOVOEWE1tHVLWb8bmbTvx8WdfCffdM3kinnj0R12Z4l1t2LILJaXlqKioMMb6xEUjJCgAEeFhd9jT8STGxyIowE+IpR482sbWjiPzWjbyC4uF2ITRQyXKhmwhLjZKGGs0GjQ3a9rYmoiIiIiIiIg6Ki0tzSxm2pjYGT4+PsL4/PnzFh+zvVprRAQsa9r09fVtNc4Gx+6HzY1E1C3IZDIsXzATLs5KIf7Fmu+wP+0w/vr620J8YP8kvPibX3Zline1e18arl1Xo76+3tgcF6MKR4CfD/p0s1lKAWDC6CHC+OLlqyguKZMoG+vQ6/VITTsmxAb3i4ePl6dEGZEtmBahNbetCEtERERERERERERERGRLIwYlYkCiWDtMO3ICDz/+MyHm5eWJj959CzKZrCvTu6N9B49g74HDAGCc7NXPxwuxURFIiIu1q1ytQS6XYfwosenv1LkLKC513JqowWDAzr0HhFhwgB/6xMVIkxDZxK69B4XxwKREuJmsGktERERERER2Qibj7ebNgbS2oqI1Vm40PUZXrtz4wAMPtBpfunRpp49ZWVnZatwajaBkX9jcSETdRoCfDxbMmizEysorsGjZo9DpdMaYk5MTPnn/HTg7O3d1im06fvocNmzZCeDWL2F3N1f0iY9BgL8fAvz97rC3Yxo2sC/cTQog23anSpSNdZxupRg5YdRgaZIhm9m1T5wtZeyoYRJlQkREREREREREREREPY1MJsOD8+8R6mwHDhxAeXmFsN2bf3sZ0VGWfyHKWo6cOI2U9ZsBADqdDjU1NZDL5RjYLwEymaxbTvYKAKOHDYDSyck41ul0+GLNBuj1egmz6rzjp87h/MXLQmz86KGQyx3rC4R0Zzv2iA2s0yePlygTIiIiIiIiou5JrVabxUxXXeyM1lZPbO1ctqBSqbBu3TokJSUZx6tWrTKOOyM3N9csxsbG7onNjUTUrYwbOQh94qKN42PHjqG4uFjY5g+/fg5DBg/s6tTadO7CJXzy1VoYDAYAt5obB/brDSeFAr3jekmYne04K5UYM1z8ORw+fgoXLl2RKCPL1NbV4et1m4VYWHAA4mMiJcqIbKGyqhpHTpwRYtMnj5MoGyIiIiIiIiIiIiIi6ol8vDyx/P7pkMmArKwsXL4sNpvNuGcyHv3RcomyM3fg8HF89vV647iqqgoGgwFJvXvB090dCoUCcbHRdziC4/L0cMfYkYOFWOa1bOw7eFSahCxQVV2DNRvEeqinhzvGDLOf2jtZrqKyCkdPsh5KREREREREZEtd1XDY1edKSkrCunXrcOnSJezatQvJyckWHa+1lSf79+9v0THJPrG5kYi6FZlMhocXzYavjxeKiopw+vRp4f5BA/rjdy/8jyS5teb46XN4/5PVxpk5m5ubUV9fj4ReUQjwa5l9obvOUgoAU8YOh6uLuILmF2u+Q0NDo0QZdV7K+s2orasTYjMnjYbMwZY5pzv7Yf8hYSZdV1cXjOPKjURERERERERERERE1MUG9UvA7EmjsW/fPiHu7OyM++bPg06nkyizW/R6Pbbu2ouvvv3eONEr0DLZa+9eUYiKCAUAxEar4Ozs3NZhHN68GZOMtd+bNmzZieLSMoky6jiDwYDVazeirr5BiD8wf4awiig5vh9S08zroaNHSJgRERERERERUffTlQ2H1dXVXXYua2rrMbK0YZLsE5sbiajb8fPxwuMPzsPevXuFIplcLsfEiRNRUSX9L2i9Xo9dew9i1ZffCoWByspKxKjCkRAbBQBwdXGBKjJcqjRtzs/HGwtmTxFilVXVWLtxm0QZdc6ps+k4cfq8EBvYNw7DBvSRKCOylR17DgrjCWNGwI0FWyIiIiIiIiIiIiIiksCXKd+ioUFsNhs7dizOXriMv7/9PtR5BRJlBhSVlOKtdz/Gxm0/CHGDwYAgf2/Ex6qMsd7xvbo6vS7l6uKM5QvnCDGNRoMvUjYItWJ7dvzUOZxNzxBiQwf0xdABiRJlRLayY88BYTxhzEjWQ4mIiIiIiIgchK+vr1msKxsprenrr79uNb506dIuzoS6ApsbiahbevfDT1FeXi7Ehg8fDo1Ojz+/8S727D8sWaGouLQM/3hvFdZt2i40XwJAkL83+vXuZVztL75XDBQKhRRpdpmxIwYhMT5GiKUdPYnzFy9Lk1AH1dTUYvXaTULMw80VD8ybxlUbuxmDwWBWzJs2aaxE2RARERERERERERERUU+29vttWL12oxCLjo5Gnz4tk2/mFRTh72+/j03bf4BWq+2yvPR6PXanpuEvb7yLq9dzzO4fMbAPekWFQ4ZbdbTecd27uREA+sTHYPzooUIsMysbX6/bZFYztjdXs7LxxTffCTFPD3csnTddoozIVgwGA7b/kCrEZkwZL1E2RERERERE1C4yGSCT8+ZA39luayVFb29vm5yvsrLSJse1tTVr1pjFli5darPHiaTF5kYi6nbSjpzAG//+SIgFBQVhyJAhAFpmwfzmuy34x3urUFxa1mV56fV6/LD/EP78xrvIzMo2u3/cyMEIDw4QGuL6JMR1WX5SkclkWLZgFlxdnIX4x198g5zcfImyap+Ghkb866PPUVtXJ8QXz50CHy8PibIiW8m8lo3rOblCbPrkcRJlQ0REREREREREREREPVVJaRl++sJLQszLyxOTJ08Wao16vR5bdu7F399+HxmXr9q0ic5gMODa9Ry89e7HWPv9NmhMGiplMmDh7CmICg8WGhtDggPh6+tjs7zsyf2zpiDAT7zWA4ePY8PmnXbb4KjOK8C7//0SGo1GiD8wfwa8PN0lyopsJfPadWSr84TY9MlsbiQiIiIiIiJyZDU1NVKn0GHbtm1rtQn0+eeflyAb6gpOUidARGRN9fUNeORnzwvFH2elEosW3AfIxX7uzKxs/PmNdzFjyniMHz0cXl6eNsnJYDDg0pVr2LxzD65mmc9OCgCzp45DfHQ4Ll/KMMbkcjnie8XYJCd7E+DngwWzp+Cr9duMscamJvzrw8/wi6d+jLDQYAmza11TUzPeW/Ul1HkFQnxg3zgMH5goUVZkSzv2iqs2hgYHoX/f3hJlQ0RERERERERERA7HYGi5WZMDzUYtkKqJR9LHS8rGJQuu29KflVSPuZR5W3Ludp7357/6I0pKy4XYu6/9CRPGjsbn67Yhr7BUuC+voAjvfPApQoICMT55BEYPHwx3N7fO53mbpqZmHDt1FqlpR5GbX9jqNkH+vli+aA4SYqPw7kefCff1jrdw1UZJmwI79jxxdXHBj5bMwzsffQWtTmeM79x7AHq9HgvunSE0p0otK1uNf3/0BRoaG4X46GEDMHRAZ+uhllyfg76uLbpmC3Xw1Dv2iPXQsNBgJLEe2kNI9Nq09D3PThvDyc5I9v4PSPp7zxIyB10zxaCXOoMexn7+biUiIrqTkpISmxzXw8MDnp6d6z2oqqqycjbdzwcffGAWe/nll7lqYzfG5sZWSfUPwx76gY3DFhEd9R9nUn7oYqm7n/t3r7yGK1evC7FXfv9LPPPkSny3fR/2HT4l3KfRaLBp+w/Yumsfhg5MwoTkEegVE2WVolF9fQMOHz+F1EPHUFzS+gqRAf6+WLFwLnrHRWPj1p3CfVGREXBzt3C2S8m+HNDxD7nGjhyCC5ezcDr9kjFWW1ePt977GD9//GFERYZbM0OL1Dc04N3/foFr19VC3M/HCw/On9bJ54+lzzkH/R1giS7+/bFzz0FhPH3yWLsqMLePhM8TR/07hYjI6hztd4c9cJwvqFkPf2/2LD3xfcGRn+OOnDsREREREXUHazZsxjffbRVi982ehocWzYNMBvzqyWXYtu8Itu87Cr1e/IJ3UUkpvv1uK77bsgsjhw7E4P59ERUZ3uFJYOvq66HOLcDZC5dw+PgpNDY2tbntpOThmD9jIlycnVFbW4fiErHxsnechc2NDiY+NgorH7wPH325Tpiwd3dqGuobGvDAgrlQKpUSZtgi/eJlfPT5GjQ1Nwvx/onxWHb/LImyIlvbsWe/MJ4+ebwD1kOJiIiIiIiIblm4cKFNjvvzn/8cTz/9tE2ObQkvLy+pU7DYtm3bkJ6eLsRmzJiBpUuXSpQRdQU2NxJRt7Hv4GG8/Z9VQmz0iCH45c8fh0Ihx9J50zA4qTe+WLcNZRXijAc6nQ7HTp3FsVNnEREWgpFDByFaFQFVRBjc3FzbdX69Xo+S0nLk5OYjI/Majp86B41G0+b2E8cMx/yZk+Hq4gwAyMoWV3W0eJZSByOTybDygfl499M1uJR53RivravHP95bhUeXL7aLWSFLyyrwwaerzWae9fZ0xzMrF8Pb00OizMiWNBoNfth/WIhNmzROomyIiIiIiIiIiIiIiKgnKiouxc9e+KMQC/D3w3tvvHKjAckAJycF5k5NxqC+8a2u4gi01D0OHjmBg0dOAAB8fbwRrYpAVGQYwkKC4ezsDGdly9dJNFotmps1KCouRXZuPnJy81FWXnHXXG9frfGmnNw8YRsXF2dEhIfe8Tg6nR4arQYajRYKuRxOSiconZwcuuFqSP9ELF84B59/u0mIHzp2ClnZuVix9D7ERqskya2xsQnrNm3HgcPHze5L6BWFx5bdD4VCIUFmZGst9dBDQmz6ZNZDiYiIiIiIiKyturq6S89XWVnZpeezRHV1NV588UUhlpycjHfeeUeijKirsLmRiLqF2to6/PjnvxJirq4u+OTfr98orrTMetknLhq/f6b1VRxvyisowvrNO4zj4MAAREWGIzI8FK6urnBWOkGukEOj0UKj0aKsvAI5uflQ5xWgsantWUlvun21xtvzL6+oFLbrFROFO9HrDdBqtWi+0UCpvFHIk8s7vmqivVAqnfDEikX4539XIyvnVnGzsakJ//7vFxg7ahgWzJ3R7oZTa9Lr9dh/6Bg2bN5pNkOph5srfr5yMYID/bo8L+oah4+fRm1dnRC7Z2KyRNkQEREREREREREREVFP9PSv/2jWWPiv//sjQoIDzbZVhQfjV08uQ9qJc9h35AwKi8vaPG5lVTUqq6px5vxFi3MM8PPFhNFDMGH0ULg4Owv3qfMKhHFkeJixtqnX61FYUoac3ALk5BVCnVeA3IJis7ocAMhkgL+vD6IiwqCKCEVURBiiIkLh6eFucf5dZczwQZAr5Pj8m43Q62+t4FhYXILX//UR7pk4FnNnTO7SVRwzLl/FF2s2oLyyyuy+pD5xeHzZAjgrlcBtK05S93H42CnU1prWQ8dKlA0RERERERFR9+Xt7W2zY9fU1JjFfH19bXY+a3v99deF5s+kpCSsWrXqDntQd8HmRiLqFl78yxu4dl1c+fAvL76APglxZtu6ujhj6bxpGDNsAPYeOonjZy9Cq9W1eezi0jIUl5bh+OlzFuUYFhKECaOHYsywQXB2FotQ6rx8YeysdEZwUEsR0mAwoLKqBjl5BS233AKo8wpRbVJYuMnTwx2R4SHGIl50RBj8/XwcZvZSVxdnPP3jB/D+52tx6ep14b6DR04gPeMKli+Zj359Erosp9KyCny+Zj2umOQDAD5eHnh65WKEhwTBYNB3WU7UtXbsOSCMBw/o2+oXBYiIiIiIiIiIiIiIiGwhL78Q336/TYgtmjcTS+6b3eY+Tk4KTBg1GONHDkLm9VykHjmD0xcyoddbt6YlkwFJfeIxcfRQ9E3oBbm89bqkaU00PDQUJ85exMHjZ3Dteq5xUte7MRiAsooqlFVU4dT5DGM8wM8Hg5L6YPyooQgJCuj8BXWRUUMGwM3FBf/9aj00Wq0xbjAYsHPvAZy9kIGl981Bn4ReNq31VlZVY8vOva2u1ggAwwb2xY+WzIOTE1ds7M527NkvjIcMTDJ+Z4GIiIiIiIjsmezGrafjY+Do0tLSkJKSYhyrVCp88skn0iVEXYrNjUTk8DQaDT787GshNm70CDzzxMo77hcVEYqHF83GgtmTcejEOew/chql5ZVWzU0ul2NI/0RMGD0M8bGqNotO6lyxkBcRHors3AKkHj6Bi5evtdnI2JraunpkXMlCxpUsY8zD3Q194mMwfuRQ9I6LtvtGR1cXZ/z0R4uxKuV7nE6/JNxXWVWNf334OcaMGIIZUycgONB2hcn6hgYcPHwCW3bubXVW2CB/Xzy9cjEC/X1tlgPZh50mzY3TJo2TKBMiIiIiIiIiIiIiIuqJTpw5L4w9PTzw79f+1K66n0wmQ0KsCgmxKlTV1CLt+HlcuHIduQXFaNZo77p/a5ycFIgICUJifDTGjRiIgIA7N0FpNBoUFBYDABqampCTV4jCilrIFdZbmbCsogo/HDiKHw4cRWJCLCaMHoYBiQlQKORWO4e1DezXG7/++Up89s1G5OQVCvcVFZfinQ8+RWhwECYkj8SoYYPg5uZqlfMaDAZcvpqF1INHcSY9o9WGVxdnJe6bNRnjRw1rs2GVug/T5sbpk8dLlAkRERERERER9TRqtRrPPvuscezt7Y1Vq1bZdJVLsi9sbiQih3fpyjXU1dULsf/+8+9QKNo3c6SnuxumjR+JqWNH4GJmFk6nX0ZOXhHyi0qg03V81lJvLw9EhYciPlaFUUP7w8fb56775OTmAQC0Oh3yC0uQV1yOI2cvd/jcbamrb8DJsxdx8uxFhAQFYMLoYRg9bADcXK1T/LIFpdIJjy+7HweOnsa6LT+YNRceOnYKh46dQr8+8ZiQPBL9+/aGXG6dwqQ6rwCpaUdx7OTZNmeIHTdiEBbMmgRXF2ernJPsV3lFJY6dEldunT55rETZEBERERERERERERFRT3T6/EVhPGxwEoI6MQmoj5cnZk0ejVmTR0Ov16OwpBzq/GLk5BdBnV+MyupaaLVaaLQ6GAwGKJ2coFQ6wcvDDarwEERFhCIqPAThIQHtrscCQH5BESqqqnH5Wg6KS8tgMABD/YI6nH973ZwM1tfHC5PGDMfkcSOhdLLPr8iEhwbjhZ+uxM7UQ9i8K9WsRl1YXII1Gzbjuy07MWLoQIwYMhCqyDC4urh06Dx6vR7FpWW4eOkqUg8dRVFxaZvbJvSKwoqFcxAY4NepayLHUl5RiWMnzwqx6VPY3EhERERERESOb+3atQgKsv5nUB4eHp3e18fn7r0F1uTl5dWl5+uMlStXorq6GkBLY+O6deugUqkkzoq6kn1+cktE1AFnTAp5qohw9I7v1eHjyOUyJPXuhaTeLftqtFrkF5YgJ78YOXmFKKuogkajhUarhV6vh9ONQp67qwsiwoJbCnkRofD17tgfABqNBtnqPFzKvA51QSG0Wh169+4Nn47VotqtqKQM32zcge+27cHIof0xe8o4+PrY56wGMpkM40cNQb/esfhi7VZcunrdbJsLlzJx4VIm/Hx9MHbkUCTExUIVEQZX1/Y/gDqdDvmFxbiek4vDx08jK1vd5rb+vt5Yfv8MJMbHdOKKyBHtTj0Eg8FgHLu5uWLsyGESZkRERERERERERERERD3N2fMZwnhgUl+LjymXyxEeEojwkECMHtrf4uO1RavV4esNW3Dg6GljzcXd3b3V5kg/Xx9ERYYjKjIMURHhCAjwg9JJCaXSCXq9HhqNFg2NjcjLL0RObj5y8vKRm1cIjbb1FSgrq2qwYdseHDl1Hg8vnovoyHCbXaclFAo5Zk4ei4F9E1pdxREAmpqbceDwcRw4fBwymQzBQQGIigxHdGQ4IsJC4erqAqVSCblc1lLX1mhQWl7R8jip86HOL0BTU3MrZ7+FqzX2TLv3HTSvh45iPZSIiIiIiIgcX1BQEEJCQqROo12qq6ttslKhr6+v1Y9pTc888wzU6pbv7rOxsedicyMRObzT5y4I40H9LS/kAYDSyQnRkWGIVkVY5Xht2bkvDT8cPIrm5lsrBHp6eppt5+rqAlVEGKIjIxAVGY7QkCC4ujhDqVQCADQaLZqbm1FYXIqcvHzk5OZDnZuPuvqGVs/brNHgwJFTOHHmAhbfOx2jhg6ATGafBaoAP1888+gDba7iCAAVlVXYtGMPgD0txbzAAGPh08/Xx9iMqtPpodFoUFffAHVeAdS5+cgtKIK2jYLn7bhaY8+0Y88BYTwxeWSHmmeJiIiIiIiIiIiIiIgsdSZdnPB1UP9EiTLpGHV+ET7/dhN+2CdOJnl7PTQ+Nhrjx4xAYkIveHmZ10lbExUZjjEjhwK4NZHpsVNncejoyVbrowVFJXjt3U8wbeIYzJ463u5XcTxw9CT2ph1HUUlZq9sZDAYUFZeiqLjUbMW9znBWKjFiSBJmTEpGoL+vxccjx7Jjz35hPGnsaLi4uAC3vWaJiIiIiIiIyDps0cB4U2VlZZeez1KvvfYatm/fDgBQqVRYt26dXedLtmOfn9YSEXWAeSHPOs2NtlZbV48132/Hxm27hcbG22cpDfDzxfgxIzB4QD8EBvhBLpff9bjhYSEYOigJQEtRq7yiEmfTLyE17SiKSkrNtm9obMJn32zEyXMX8dD9s+x+FcekPnHYc/AYDp04i/qGxla3NRgMKCopRVFJKY6dsqyYJ5fLMKhfAqYkD0NcdKRFxyLHYzAYsNOkuXHa5HESZUNERERERERERERERD1RTU0trmblCLFBVli50ZZ0Oh22/nAQ2/Yegk6nQ11dnXC/v78fxo8ZgQnJIxERZtns+QqFAqqIMKgiwjB3xhScPJOO1LSjuJ6TK2yn1xuwfU8azl64YverOE4cMxwTRg/D5avZSD18AmcuXIJeb/1Gs5CgAEwYPRSjhg6Au5ur1Y9PjmHHD2Jz4/Qp4yXKhIiIiIiIiKhn8Pb2RnV1dZecy8fHp0vO01EpKSn46KOPALCxkdjcSETdwJnzjtfcePp8BlZv2Iaa2jrU1tYK93l6eiIpMQETkkciKTGhXQ2NbZHJZAjw98Pk8aMxadwoXM7MQmraUZxJz4Berxe2PZ+RiVfe+gCL5k7D6GEDYaeLOMLf1xsL50zFvdMm4MTZi9h3+ARy8gqtfh4fL0+MGzEQY4cPhK+Pl9WPT47hcmYWcnLzhdh0NjcSEREREREREREREVEXOnfxsjBWKBRISkyQKJu7a2xqwvufr8Olq9dbxo2N0Gq1AFrql3GxUXjtld8hNDjY6ud2VioxevhgjB4+GNdzcrF67Uao8wqEbQqKSvD6e59i5dL7MHSg/daWZTIZ+sTHoE98DCqrqnHg6GmcvXAZ+UUlZrXejvDx9kRCbBTGjhiM3nHRkNlrYZi6TH5hsTBOTIiTKBMiIiIiIiLqOBns9kvfXcqxHgOVSoX09HQhVlVVZXFzX01NTavnsjfbtm3DSy+9BABISkrCJ598wsbGHo7NjUTk0AqLilFsshqhPTc3GgwGbN61H1t27zeOb29uDA4MwK+efRITx42x+rllMhn6JPRCn4ReqKyqxvrNO3DspLiqYUNjEz7/dhNy8gqw+N7pkMvt9w89Z2clxgwfiDHDB+K6Oh+HTpxFVk6+RcW8AD8fREeGYmj/PhjUN964gib1XCVl5cJYLpcjRhUhUTZERERERERERERERNQTmU72mpjQC66uLhJlc2d19Q349ydrcF19a/LIm/VQL08PDOrfF6qIcJs0NpqKiYrEr575Cbb/sB9bd+2DTqcz3qfT6fHf1evR0NiIsSOH2DwXS/n6eGPutAmYO20CNBot8gqLkJNXiJzcAuTkFaCsohoarQZabcs1yuUyKJ2UcHdzRWR4CKIjwqCKDEVURBh8vDwkvhqyN/G9opFx+apxvGHzDsy8Z6KEGRERERERERF1b5GRkWbNjdZYybGqqsoslpSUZPFxrSk9PR3PPvssgJbc1q1bJ3FGZA/Y3EhEDs20kOfu7oa42GiJsrkzvd6AbzftxN60Y8bYzVlKnZyckJSYgIiwEAzq38/mufj6eGPlQ4swdGASVq/diOoacfXIfYdOoKGxCSsWzXGIBr8YVThiVOEAAE1zM/IKi5GTV4Sc/ELkF5agoakJWq0OGq0WCrkcSqUSzkonBAX4ISoiFFHhIVBFhMLT3Q0wdH6WU+p+4mOjIZfLjQ2zer0eG7bsxLJF8yTOjIiIiIiIiIiIiAiAwdD5fXvibN6WPF6WkvTxtuS6LczboueoJSeW8PG2wc/6THqGMLbXyV7rGxrx9kerkVtQJMTr6uqQ0CsG8b1a6i6qyPAuy0mhUGD2tEkYmJSIz1PWC6s4GgwGfLluC/QGA8aPGtrxg0v0ulY6KxETFYmYqEiz+/R6AwwG/Z1rvJa+F0r18rL4LVyi35lS/u5pp+WL78Mf/vyGcZyyfhPe/ttLcHGxoInaAa67Vd38Z01WYun7v6M+V/j66GKO+u8HCcnkUmdAXYXf7yOyDUveR/m6JOqwAQMGYPv27UKstcbEjjJtkLS3VRvVajUeeeQRAEBycjJWrVolbUJkN9jcaFek/EehhP8YdtgioqV5S/SBTzcrVJs2Nw5MSrS/ZjyZDAaDASnfb8f+wyeEu2praxEcFIAB/frA1cUFXp6e8PHpuiWVB/Xvi/he0VizYYvZKo5HT52HRqvFow/eD7m8g/9osfh51vnnuNLZuc1inq3PLbPgdW1w5A9yu9n7yu1CQ4IwffI4bNudaox9nrIByxbPt+zAjvzzpi7E5wk5Ain/nnZUPfW6HRV/XtQV+DxzKPxbnoiIiIiIJHC2lZqovWlqbsa7n64xa2z09HDHyKEDhH9PRak6W8frvMjwUPzqmZ/gm++2IjXtqHDf1xu2wtXFBSMG29cs9p0hl8sA2Fm9nOze8iVic2NlVTU2bf8BC+fNkjArIiIiIiIiou6rtdUU1Wq1xcc1PUa/frZfdKm9qqursXLlSlRXV3e6sXHbtm1ISUlhU2Q3xKlKiMihmTY3Dkqyz1lKv9u2x6yxUSaTYXD/RAwfPACuN2Y8jFJFQNbFjWIe7u5Y+dAirHxokVlj6KlzGfhq3RbHbrwjstDyJWIj4659acg3KcwTERERERERERERERHZgk6nw7mLl4XYoP721dxoMBjw39Xf4Vp2nhD39fHGkysfNJsoRhUZ0ZXpGSkUCiy9fw5mTp0gxA0G4NM13+PS1euS5EUkteioSEwcO0qIffb1OomyISIiIiIiIur+kpOTzWI5OTkWHdN01UagZYVIe/HII49ArVZbtGJjWloazp8/b+XMyB6wuZGIHJpZc2N/+2tu3Jt2DDv2pgkxuVyORx5aCB8vD6GZUapCHgCMGDoQT658CEqlUoinHT+DTTtT29iLqPu7b/Y0eHp4GMd6vR5frd0oYUZERERERERERERERNRTXM3KQV1dvRCztwlfDx47i/MZmULMx9sLz/30x9A0a4S4UumE0JDgrkxPIJPJMG/WPZg9bZIQ1+v1+PybjWhsapImMSKJPfzAAmG8ZedelJaVS5QNERERERERtZtMztvNm4MxXb3R0pUbW9t/5syZFh3TWlauXIn09HSLGhsB4Pz581CpVFbMjOyF472CiYhuaGxsRMaVq0JsUH/7WToZAAqLy7Bu8y4hJpPJ8OjyxUjqE29WDIhSRXZlemaSEhPw0x8vg5OTkxDftucgsnLy2tiLqHtzd3fDovniH/effb2eK5oSEREREREREREREZHNnUkXJ3sNDgpAaEiQRNmYK6uowtqte4WYh7sbnv7JjxAY4I8cda5wX2R4OORy6b+qMmf6ZEwaN1qIlVdWY92W3RJlRCStRfNmwdXVxTjWarX4mhO+EhEREREREdnM0qVLhfGFCxcsOp7pioYqlcouGgFfeuklpKWlWdzYCADp6eno37+/lTIjeyL9J8ZERJ104VImdDqdEBvQr49E2ZjT6/X4fO0WaLVijssXz8eQgUlQ5+YLcaXSCSHB0hci+yT0wqPLFwtFRYPBgM++2QiNRithZkTSWbHkPmF8/uJls5VjiYiIiIiIiIiIiIiIrO3M+QxhPCgpUaJMzBkMBny5fgeaTFZnfOShRQgPbVmdUZ0rTqCqiozosvzuRCaTYdG8megdFyvEDxw5hYwrWRJlRSQdb28v3D9nhhD7LGWdRNkQERERERERdX+mzY1qtRrV1dWdPt7BgwfveHwpfPjhh0hJSbFKY2NaWhoA2EXDJlkfmxuJyGGdPifOThAXGw0vL0+JsjG3+8Axs9UOp4wfgzEjhwIwL+RFhIVBoVB0WX53Mqh/X8ydMVmIFZWUYePOfRJlRCStiWNHQhURJsQ+X/OdRNkQEREREREREREREVFPcdZk5caB/ftKlIm5g8fOIuNqthBLHjkUSYkJAFpWfssvLBTuj1LZR3MjAMjlcixfch9cnJ2F+BdrN6GxqUmirIiks2Lp/cL42MmzyLh8VaJsiIiIiIiIiLo/0wbEmw18nXHo0KE7Hrs9qqurkZKSgtdee82iXABg27ZteP311zFjxgyLGxtvHg8AkpKSLD4W2R82NxKRwzpzXmxuHGRHhbzC4jJs3JkqxIIDAzBv1lTjOEedK9wfpYrsktzaa9qkcYiKDBdiu/cfMWvYJOoJbha3b/fVt99Dq+VqpkREREREREREREREZDv2unJjVU0d1m0TJ0b19fHGwntnGscFhUXQanXGsUwmQ2SEWH+UWmCAH+6bM02IlVdW4/vtnPSVep5pk8chJDhQiH3O1RuJiIiIiIiIbObxxx8XxikpKZ06zrZt24RVHx977DF4e3t36BhqtRpTp07FSy+9hI8++ggrV67EM88806l80tLS8Oyzz2LGjBl45513OnWMm6qrq5GWloatW7cC4MqN3RWbG4nIYZ05L85Sai/NjQaDAV+t32pWqFux9D4435j1U6fTmc1Sqoq0r0KeQqHAww8sgNNtq0kaDAZ8/u0m6HR6CTMjksaKJfOFcVFxKXbuPdjG1kRERERERERERERERJYpr6iEOq9AiA0e0E+ibET7j55GY1OzEFu+ZD7c3FyNY3WuOGlqUGAAXF1dYW/GjxmBhLgYIXbw2CnU1zdIkxCRRJycnLBssVgT/TxlA/R6fj+AiIiIiIiIyBZUKpWwwmJaWhrUanWHj/PBBx8Y/9/b2xsvvPBCh4/x7LPPCg2SALB9+/YOr+CYnp6OlStXGvcfMWJEu299+vQxu40YMQIrV6405sbmxu6JzY1E5JAMBoPdNjdeV+cj87q4KuPk8aMRFxttHJvOUgrA7mYpBYDw0GDMnj5ZiBUWlyL9UqZEGRFJJ7F3HEYMGSjEvlizQZpkiIiIiIiIiIiIiIio2zubLq7a6OysRJ/4WImyuUWn0+HgsbNCbOTQQejXJ0GI5ajF5sYoVaTNc+sMuVyOFUvug1x+6ys0Go0Wh0+ekzArImmsWLpAGKvz8rHv4BGJsiEiIiIiIqK7kclkvN24Oarnn39eaNh79tlnO7T/tm3bkJ6ebhy//fbbncrj9mPc7uDB9i8EU11djUceecQs1t7b3bCxsfticyMROSR1bj4qq8RfYPbS3Ljv8Elh7O/rg3kzpwqxHLXY/BgcFAg3Nzeb59YZ0yaNRURYiBBLPXxComyIpLXcZPXG9Zt3orq6RqJsiIiIiIiIiIiIiIioOztj0tyYlJgApVIpUTa3nLmYiaqaOiE2dWKyMDYYDGYrN6rscLLXmwID/DFkoLgqZurhE9DrDRJlRNT10i9exvMv/sUsvnXn3q5PhoiIiIiIiKiH8Pb2xqpVq+Dt7Q2gpcnwtddea9e+6enpePHFF43jt99+G8nJyXfY4855tCYqKqpd+1dXV2PBggXtalLsLDY3dl9sbiQih2S6aqOPtxei7WCmz9q6epw8KxYZJ4wdCWdnZyFmVsiLjLB5bp2lUCgwZfwYIXbh8jUUl5ZLlBFR1zMYDFi/aQfeem+VEG9sbELqoWMSZUVERERERERERERERN3ZmfNi3XFQkn1M9pp65LQw7hWjgioiTIhVVFSirr5eiNnryo03TRgzUhgXl5bj8tXr0iRDdqGpqQl6vV7qNGyusqoav/jdKxg0fjZ27zNfjcHH20uCrIiIiIiIiIh6DpVKhXXr1hmb9z766CO89NJLd9wnLS0NjzzyiLGZ8O2338bMmTM7ncPzzz9vFvP29sasWbPatf/rr78OtVrd6fO3R79+/e6+ETkkNjcSkUM6ky42Nw7q39culpNOO34WWp3OOHZyckLyyKHCNi2zlOYLMXuepRQAhg3uD3eTlSX3HznZxtbUU9TU1MJg6P6z1V68lIkZCx/Bwh/9DNdzcs3u9/fz7fqkiIiIiIiIiIiIiIio2ztz/oIwHtg/UaJMbikoLsPla+KXlCYkjzTbLkct1lQ8PTzg6+tj09wsFd8rGmEhwUJs3+ETEmVDUmpoaMTzf3gV/rGD4BnRFw899jR27tkP3W3fBegO9Ho9/vt5CnoPn4J/vPdxq9c3fcp4PPPEI12fHBEREREREVEPo1KpsGvXLjz22GMAgJSUFIwYMQKvvfYa0tLSoFarkZ6ejpSUFKxcuRIrV65EdXW1sTHSksZGAFi6dClefvllqFQqeHt7Izk5GevWrWtzRUdTlZWVFp2/Pdq7iiQ5HiepEyAi6gzTlRsH9Ze+C1+v12P/kVNCbOigJHh6eAixiopK1NbVCTF7n6XU2dkZY0YMwe7UNGPs0PEzuHf6RDgrlRJmRlI4d+ESnvrlS0g7ehKR4aFYsfR+PPLgAiTExUidmlVVVdfgT//3T/zrw8+h1WrN7pfL5fjtL57EmBFDJMiOiIiIiIiIiIiIHJO1J4yTfuJHaicpJwu0aIJQS/OW6jlqad7ST+6o0WiQnnFFiA1KsmVzY/t+VsfPiKtJenq4Y8jAJLPt1Hkmk71GhtvFZLV3IpPJMCF5JFLWbzLGzl28jIamZri5ukiYmZVZ/HOwYH9L3gstTbudpz564jQefvIXuHTlqjG2+tvvsfrb76GKDMePHliIR5YtRlxsdPsOaOnjbdHvj7bPffjYSTz96z/i+Kmzrd4fGR6G11/5HZbcP8fuX7sEu32etePEFuxrKUf9+0j6v1GkIdFjJulrS0p833ccjvocs5DMQdf1MXT/FcHJwfE5SmQ3XnjhBTzxxBPYunUrUlJS8NFHH+Gjjz4y2y45ORlLly61uKnxdkuXLsXSpUs7te8777xjtTyo57GP5kaD3vwXopQfjDnq3/uSFqUsIeXPWsIPLyx6zCUqFgDSvjZve8xMZykd1D8Rtnset++as3IKUFZRJcQmtjJLqTo3Txh7uLvDzwFWfhs/ZoTQ3Fjf0IgLl69hsB3MEGtVEn34IYNl/zDsis9DtVotXvvnh/jj39+BRqMBAOTmF+Kvb72Hv771HsaNHo5HHlqAxfNmwcvL0/YJ2Yher8cnq9fid6+8geKSsla3GT9mON7+64sYPED6xuou5bAfvEvJQR8zi3/WDnrdluixhS0iIiJHY2+/c+0tHyIiIiIisheXMrPQ3KwRYoP695Uom1uu5xYI45FDB0HpZP7Vk6LiYmGsioywaV7WMnLYQHz7/VbjCnZ6vQHqvAL07maTfJK5pqYmvPx/b+Nvb70Lvb712q06Nx+vvv5PvPr6PzEheRRWLluMRfNnw9PTo9Xt7VFBYTF++/Lf8enqta3e7+LijBeefgK/+Z+n4OHh1sXZEREREREREREAeHt7C42GarUaarUaPj4+8Pb2hkqlkjhDIuuyj+ZGIqIOqK2tQ+a1bCFmF4U8tTj7aGhwEGKizFdkLC4pFcaREfY/SykABAcFIKFXDK5cu26MXVfnd7/mRmrVpSvX8MjPXsCRE2fa3ObA4eM4cPg4nvnNK1h070w88tBCTEgeAbnccWbLOnL8NJ75zSs41sYMpRFhIXjt5d9gKWcoJSIiIiIiIiIiIiIiGzpz/qIwjgwPhb/EE6YaDAbk5BcJsV4xrX+RSumkFMZtNYvZGzdXV4SHBkOdd6uJMyevkM2N3dyZcxfw8JO/wNn0i3ff+IbUtCNITTuCn//qJSyePwcrly3G+OSRdltDbG5uxjvvf4KXX3sHNTW1rW5z35zpeOPVP6BXTNSNCCdlIiIiIiIisn/yG7eerns/BiqVig2N1K1171cwEXVL5y5cguG21Y3kcjmSEntLmFGLnDxxltJeMapWCxdKpWMW8gAg1qQ4aXrN1P3o9Xr84z+rMGTSvXdsbLxdfX0DPktZjynzlyNh+FS8/No/ka3Ou/uOEiosKsGPf/5rjJmxuNXGRmdnJX733FPIOLIDDyyYa7dFSSIiIiIiIiIiIiIi6h7OpGcIY3uY7LW8shp19Q1CLCoyvNVt/f39xH0rKm2VltWZXhNrot2XVqvFq6+9gxFT7m21sXH4kIH4v5d/h0H9+7V5jLq6enzy1TeYOGcJEoZOxKuvvQN1bn6b20th2669GDB2Jl546S+tNjYm9o7D9rWfYf0XH9zW2EhERERERERERNQ12NxIRA7HdJbSPgm94ObmKlE2t+TkFwrjNgt5JjOqlldU2Colq4uKEK9JnVcoNJpS93Lteg6mzF+G537/ZzQ2Ngn3yeVyPPezR/Hsk48gMMCvjSMAWdm5+OPf3kGvIZMx7f4f4ctvvkO9SdFbSs3NzXjj3/9Fn5HT8Mnqda1uM3/WPUhP24pXf/8cPDzcuzhDIiIiIiIiIiIiIiLqic6a1EQHJiVKlMktpqs2uru5IcC/9TqRWU203IFqoqbNjblsbuyOLl66guTp9+PFP78OjUYj3KdUKvHK75/HoZ0b8MIzT+L0ga04lboFzzyxss3nPABczcrGi39+A9EDkjH9/uVY/e13aGhotPWlCPR6PXLzCrDv4GGs+nIN5j34GGYtfgSXM6+Zbevl5Yk3Xv0Dzh7YhulTJnRpnkRERERERERERDc5SZ0AEVFHmTY3DkqSfpbSxqYmFJeWC7G2mhv9TAp5lVXV0Ov1kMvtv9/c9Jrq6htQXlGFAH9faRIimzAYDPjg06/x/Et/RV1dvdn9veNi8cm//w+jRwwBAPz9f1/A5h178cnqtdiycx90Ol2rx9ydmobdqWnw9vLE0gVzsPLBhRg1fHCXroLY1NSE6zl5uJatRua1bLz38ZfIuGJeyAOAPvG98NZffo+ZU1nIIyIiIiIiIiIiIiKirmW6cuPgAdLXRHPyTCd7DWuzzuPn6yuMHXnlxuLScjQ0NsHN1UWijMja3vnPx/jV//4VTU1NZvcNTOqLT997E4MHJgnxwQOT8PbAJPzfy7/Fpm27seqrb7B1517o9XqzYxgMBuzcsx879+yHj7c3Hlw0DyuXLcaIoYOsUhttaGjEtes5uHY9B1evZ+PadTWuZmXjWnYOsrLVaGpqvusxVi5bjL+8+AJCQ4ItzoeIiIiIiIiIiMgSbG4kIodj1tzYX/pCnjq/CLcvYCiXyxERFtLqtqazlOp0OlRX18DX18eGGVpHgL8vPNzdUHfbynvZuQVsbuxGSkrLsOLJX2LHngOt3v/ME4/gL3/4Jdzd3YwxZ2dn3D93Ou6fOx2FRSX44pvv8MlXa3HhUmarx6iuqcWHn6bgw09TkJjQCz96cAFWLLkP4W28ZjrCYDCgvKKypYB3Pcf436xsNa5m5SA3/+6rjXp5euClXz2Npx9fAWdnZ4tzIiIiIiIiIiIiIiIi6oii4lIUFZcKMXuY8LXIZLJXVUTrk70C5jXRmtpaaLVaODnZ/9dUwsNCIJfLhaa1opIyxKjavl5yHN9+txnP/uaPZnG5XI7f/OKneOlXz8LFpe1GVhcXFyycPxsL589GQWERPk9Zj1VfrkHG5autbl9VXY3/fPwF/vPxF+iXmICVyxZj+ZL779hUaDAYUFxS2tK8eK2lafFq1o0mxuvZKCgs7vB13zRy2CD88+9/wshhgzt9DCIiIiIiIiIiImuy/0+NiYhuo9frcfaCOEupPTQ3FpWIhbywkCAolcpWt3V3d4eLszOamm/NllheUeEQzY0ymQyqiDBhpbuiktI77EGOZtlPnsOufQfN4jFRkfj4n3/DpHGj77h/aEgQnv/5Y/jlzx7FsZNnseqrtfh63SZUVde0un3GlWv47cuv4/evvomZU8fjkYcW4t4ZU+5YMNRqtcjJzRcaGK9dz8G1Gw2M1TW1Hbvo2zzy4AL85cXnERoS1OljEBERERERERERERERWeJMujjZq7u7G+JioyTK5pbmZo0w9vR0b3NbP5PmRoPBgIqKSgQFBdoiNatSOjnBzdVFmPC1WaO5wx7kSPYdOGIWS+wdj0/fe7PDDX9hoSH41bNP4oVnnsCR46ew6stv8PW6jahuozZ6IeMKXnjxL/jNH/+O2dMm40cPLoSHh3tLE2OWuAJjXV19Zy6vTcFBgfj7H3+Nhx9YCLlcbtVjExERERERkYRkspZbT8fHgMihsbmRiBzKtevmH+LbQ3Pj7Y2KQMvKb22RyWTw9/dDQWGRMVZeUYlesTZLz6o8Ta6Nhbzuw2AwYF/aUbP4Tx55EK/98dfw8vJs97FkMhlGDhuEkcMG4c1Xf4cNW3bik6/WYte+tFZXTtTr9diycx+27NwHfz9fPLToXtw/ZzrKKypbGhiz1ci60cyYrc6HTqez6FpNDR88AO/87UWMHjHEqsclIiIiIiIiIiIiIiLqqNPnxObGAX17Q6FQSJTNLc0arTB2bmOyV6BldTtPDw/U1tUZY+UO0twI4MZEtreaGzUm106Oy99PnHR4UP9+OLRzA9zcXDt9TJlMhtEjhmL0iKF46y8vYcPm7Vj15TfYve9gq7VRnU6Hjdt2YeO2XZ0+5924ubmiV3QUesVEIXnkUDz14+Xw8fG22fmIiIiIiIiIiIg6i82NRORQzpwXC3mBAf4ICw2WKJtbNFqxmNXWqo03+fn6iM2N5RU2ycsWTK+NhbzuQyaTwd/PB0XFt1bjfOPV3+EXT/3YouO6ubniwYX34sGF90KdV4DPvl6PT1avxdWsnFa3L6+oxL8+/Bz/+vBzi857J4EBfoiLiUJcbBTmTJ+MpffP4QylRERERERERERERERkF86mZwjjgXYw2SsAs8kn79Zw6efnIzQ3VlRW2iItmzC9NmtPvEnSie8VI4ybmpstamw05e7uhocW34eHFt+H7JxcfPb1Wnzy1be4dr312qglQoIDERcTjV436p69YqKM49CQIMi4agURERERERERETkANjcSkUMxbW4cPKCvXXwgr9OKxSynuxTy/P38hLEjFfJMr03LQl63Eh8bLTQ3Wvv1pYoIw+9/+VP87rmncODwcaz6ai2++W6r2YqslnJyckK0Khy9olXoFROFXjEqxN34b69oFby9vax6PiIiIiIiIiIiIiIiImsxrYkOSkqUKBORk5P4FROt9s51Qn8/P6hz841jR5rwVWsyue3d6r/kOEybG69dz4FOp7PJ6qjRUZF48VfP4vfPP439aUex6stv8M13m1Ff33D3ndEy8XBsdEt9My72tibGGysyeni4Wz1nIiIiIiIiIiKirsbmRiJyKGaFPDuZpVThJBY6TFdyNOXv5yuMyysqrZyR7ZgX8virpDuJj43GwSMnjOPMa9k2OY9MJsP4MSMwfswIvPPXF/Ht99vwyeq1SE071u5jeHt5Ii42CnExUYiNVt0o5LX8VxURZlZgJyIiIiIiIiIiIiIisneNjU3IuHJNiNlLTVRpVhPV3HH7blUTVbLu1F3ExUYL4+bmZuTmFSA6KtJm55TL5Zg4bjQmjhuNf/7fn/DNhs345KtvcfTkGXi4u91oWrzRvBgTZRxHhIdCIZfbLC8iIiIiIiIiIiJ7wE9ficihmM9Sah+FPGezWUrv0tzoL67cWF5RAYPBYBerUN6NaeOmUslZSrsT02Le1SzbNDfeztPTA488tBCPPLQQV7Oy8cnqdfh63SZkq/MRFhKEuNgbzYsmKzD6+/k6xGuGiIiIiIiIiIiIyHYMkuxqMX6227UMFvywLf5ZSfQclfI5ZtHjDVy4dAU6nbgi4sB+vSHti7aFi4uzMK6urr3j9v5+5jVRR9Dc3Iz6hkYh5urs3MbWjkjC14clp7b4JdBygKDAAHh5eaKm5tbzNzPruk2bG2/n5eWJH69Yih+vWGr77whY8n5kjf2JyPosec+Q8jVt6XudRblL+MtHqr9J+f7dg/Df1h0n5XuhhJNWGPTSndsSfMyIiIh6DDtpbjTA7A9Gyf5Bavnukp3YYT+AkLRy2vldLX28JXvMpPwHrWWPWWVlNbLVeUJsUP/E9v0sbPxhkWkhr+ouhTw/X19hrNFoUVtbBy8vT2unZnWmRUoXZxeYP6+kfH1Y+g9ax/yAT4bO/2PacNtrKL6X2NyY2a7mRuu9vuJiY/DK757DK797rgsafi35QoVjPk+IHAJfX0RERN2fI/++t3Yhk4VRIiIiIiIyYTrZa68Yld3UEMOCAnDqtnFObv4dt/czWbmxsqoaer0ecjtfiS43v1Con8lkMoQEB0iYEVmTTCZDfGwMTp09b4xlXruOqRPHSZILERERERERkUVkMk4mB/AxIHJw9v2JMRHRbUwLeUqlEokJcRJlIwoNDhTGhcUlaGpqbnN7b28vODmJKx5WVFbaIjWr0uv1UOcVCLGwkCCJsiFbiIuJEsbXc/Kg0WgkyYXFPCIiIiIiIiIiIiIi6mnOpGcI48H9+0qUibmoiFBhrM4rgF7f9qQt/ibNjXq9HlVV1bZIzapycsV6aGhwIFy61cqNZDbh67X2TPhKREREREREREREtsDmRiJyGKbNjf36xMPZTopIqrAQoRHLYDAgt6Cwze1lMpnZ6o3l5RW2Ss9qSssq0NDYKMSiIsIkyoZswbSQp9PpzFZMJSIiIiIiIiIiIiIiIts4a9LcOLB/okSZmIsKDxHGjU1NKCkrb3N7Nzc3uLq4CLHyCvuviZquSGna1EmOL75XjDDOvHZdkjyIiIiIiIiIiIiIzY1E5EBMmxsH2dEspc7OSoQGBwgx06KXKX8/P2FcXlFp7bSszvSavDw94OvjJVE2ZAt+vj5ms+hmZnGmUiIiIiIiIiIiIiIiIlszGAw4c15sbhyUZD81UR9vT3h7uguxO9VEZTIZ/P0dsCaaZ9LcGMnJXrub+NgYYZyZdV2SPIiIiIiIiIiIiIjNjUTkQM6kmzQ3JtnPLKUAEG2ygqH6Ls2Nfr4+wtgxZykNE1aspO4hPlZcvTHzGpsbiYiIiIiIiIiIiIiIbE2dV4CKyiohNsiOVm6UyWRmqxjerY5kWhOtsPPmxrr6ehQWlQixqAg2N3Y3pis3Xs3Khl6vlyYZIiIiIiIiIiKiHo7NjUTkELRaLc5fvCzE7GnlRgBQmRTyrmblwGAwtLm96SylpoVKe3T1ulicNC1eUvcQ38ukuTErR6JMiIiIiIiIiIiIiIiIeo4z58XJXn28vRCtipAom9bFmKxiePzUOTQ1Nbe5vb+fY63ceOjoKaHJzUmhQGR4iIQZkS2Y1kMbGhpRUFgsUTZEREREREREREQ9G5sbicghXM7MMiuK2VtzY6wqXBiXlJXj8tWsNrf39/MVxuXl9r1yY15BEbKyc4VYTJR9FVPJOuJio4Tx1Syu3EhERERERERERERERGRrZ85nCOOBSX0gk8kkyqZ1Iwf3w+0pNTQ24vjpc21ub1YTrbDfmqher8f+Q8eE2OABiXBxdpYoI7KVsNAQuLm5CrHMa9elSYaIiIiIiIjIIjK0tAX19Jt9fYZGRB3D5kYicghn0sVZSsPDQhAY4C9RNq2LjgxDSJCYU2rasTa2Np+ltKGxEQ0NDTbJzRpS044KYy9PD/RNiJMoG7Kl+FiTlRuvsbmRiIiIiIiIiIiIiIjI1s6mmzY32tdkrwAQ6O+Lfgm9hNi+g0dgMBha3d7fX6yJVlRWtrmt1DKuXEVJWbkQmzhmuETZkC3JZDLEx8YIMTY3EhERERERERERSYPNjUTkEExnKR1sZ6s2Ai0FkAmjhgqxM+cvorKqutXtfXy8IZeLb8PlFZW2Ss8iDY2NOHryjBAbO3IInJwUEmVEthTfS2xuvJathk6nkygbIiIiIiIiIiIiIiKinuGMSXPjoP6JEmVyZxNGDRbGufmFuJ6T2+q2fr6+wlij0aK2ts5GmVnGdOLaiLBg9IqOlCgbsrX4XjHCODPruiR5EBERERERERER9XRsbiQih3DmvLhy4yA7bG4EgFFDB8BZqTSO9Xo9Dh450eq2CoUCPt5eQqzCTpsbj544i6amZuNYJpNh3Mihd9iDHJnpyo0ajQbqvAKJsiEiIiIiIiIiIiIiIur+6urqkXktW4gNSrLP5sak3rEI8PMRYtt/2N/qtl5enlAqnYRYeUWFzXLrrPzCYpy7cEmITRg9DDKZTKKMyNZMJ3y9mpXdxpZERERERERERERkS2xuJCKHYDZLaZJ9Nje6u7lixOAkIbb/0DE0NDS2ur2/n58wtsdCnkajwZ79h4TYgL4J8DcpWFL3ERjgD28vTyFmWkwnIiIiIiIiIiIiIiIi6zl38TIMBoNxLJfL0b9vbwkzaptcLsf4kYOE2Nn0DJw8c95sW5lMZrZ6Y3m5fdVEdTodPvt6nfD4u7o4Y8SQARJmRbZmtnIj66FERERERERERESSYHMjEdm94pJSFBQWCzF7XbkRACaMFlc0rK6pxdqN21rd1s/PVxiX2+HKjZt37kVxaZkQmzBmuETZUFeQyWRmM5VmcqZSIiIiIqL/Z+++w6K80jaA3zPM0KV3EFDsFQQE7Bo1JvbEbjR90zbZZLO72W93s5st2Wy2pSebahJT1Bh719gVFJCiYqNJ76B0Bub9/jASzwwowwy8M3D/rmuu5DxvOc8MQ5GH5xwiIiIiIuNIkukfxiVkxMPYqeV6zmSw3voehcKIR+eknBMXex08oB/s7Gy7L2+FYY/xkaPhYG8nzL5u0w5UV9foZaW74Gtl1TUDn1fX2n/4BHLyCoTY5HGRsLWxvsOVxrxP5HwYS87PTSPovIdD+gULh9Ozsm9k2NZ7XtbXm4iI5GOh3wMM/LnOpA8is2fJP4cbQaG0zAcRdYyc3/vN7UFEFquHfuc35hepcv4y1VLzhmmLsN1avDT2NTfiwaJth+kW8uzsbDEwJFieZDqgr583hg3qL8ROnj6D8xev6J3rptvcaGarlGbn5GH/oeNCLNDfF0MG9G/niptk/CWAnD/YyvW1sAu+LgzoFySM2dxoYkZ9rI15UK9iRl9TiIiIyAz1oH+/GPa8tcY9+PM4ERERERF1kYTks8J49IghMmXSMQ72dlg6b7oQq6mtw7rNO/XOdXVxFsYVleZTEy0oLMaufYeEmLenO+69a6JMGVF30d25sbq6BqU6i/4SERERERERERFR1+uhzY1E1JMkppwTxiOGDoKVlZVM2XTM8gWz9Fby/Pq7raivbxBi5rxKqUajwZfrNkO65Q9PrayUWLV4LpRKrm7R04XoNDdmZLK5kYiIiIiIiIiIiIiIqKucSkwRxhFho2TKpOPCRw5B6PBBQiwp9bxeo6abm1gTrais6urUOqSlpQVfrt+M5paW1phCocDqJfOgVqtkzIy6Q4C/L6ytxZp+ema2PMkQERERERERERH1YmxuJCKzF5eQJIzHjhktUyYd5+7qjPvunSbEqq5dx8Ztu4WY7s6N1TU1aGpq6ur0OmTnvkMoKikVYvfeNQn+vt4yZUTdSXfnxozsHJkyISIiIiIiIiIiIiIi6tmuV9fg/MUrQiwmMkymbDpOoVBg2bwZcLC3E+Jfb9iKzFtqS7o10YoK+Xdu1Gq1+HL9ZuTkFQjx6ZOi0S/QX6asqDtZWVmhf3CgEGNzIxERERERERERUfdjcyMRmTVJkhCXkCzEoiPMv5AHAOMjQzFkQLAQi41PwoHDJ1rHrjqFPACoNIOVSmPjk7Dv0HEh1tffBzOnjJMpI+puIf3EQl5Gdg60Wq1M2RAREREREREREREREfVc8UmpkCSpdaxWqxE2cpiMGXWcUx8HLJ03XYg1NjXhvU+/Ql5BEQDAzVXcubGhsRH19fXdlqMuSZKwYcsuxJ9JFeLenu6YM2OyTFmRHAb0DxbGbG4kIiIiIiIiIiLqfmxuJCKzdjU3H8UlZUIsOiJUnmQMpFAosPK+e2FrYy3EN+3YixOnEgHcKEz2cXQUjlfI3NyYfDYNX23YIsSsrJRYvXgerKys5EmKup3uzo319Q0oLCqRKRsiIiIiIiIiIiIiIqKeKzY+WRiHjRwKW1sbeZLphPCRQzA2bIQQq69vwJsfrEF2Th6cnZ2gVIp/niJXTVSr1eKbjdtw9ORpIa5Wq/DQsvlQq1Wy5EWmVd/QiIzsXJy9cAVnzl7AqaSziE8+j+Tzl5B2ORPFpeXQaiUM6C/WRLuzuTG/oAj/fudDvPPhGtTW1nXbvEREREREREREROaGv5UlIrMWG39GGLu7uSJEp+nKnLm7OmPpgln4Yv02If71d1vR3NyMyeOj4ObqguqamtZjlVVV3ZzlT+KTUvHFt5uElWEBYME9d8Hf11umrMjUmjQalFdeh0ajgaa5Bc3NLVCprKBWq2BnYw03F2f4+njBzs4W9fUNrdelZ12Fv59Pt+SYev4i9h06jojQEZgyIbpb5iQiIiIiIiIiIiIiIpLDqYRkYRxlIYu93qRQKPDA/bNRW1eP85cyWuN19fV468PPsfDeGbBWq9HQ2Nh6rKKiEv5+vt2aZ0NjI77asBVnUs4JcSsrJX62ajGCAvy6NR8yjYbGJuTkFyI3vwhX8wqRk1+IkrKKO15na2ONnIJSIXY8Lh6Hj8Vi8oRoKBSKLsm3vr4B/3n3I7z2xvuoq7uxg+mOvQex5/svu2xOIiIiIiIi6sEUihuP3o6vAZFFY3MjEZm1OJ1VSqMjQi3uF/pRYSNRXV2LTbt+EOLrN+9EeUUlWrRaIS7HKqWSJOHw8VPYuG23XmPjjMkxuGsim8ssVZNGg/yiMuTkFyEnvxg5BcUoLCmDViu1e42NtRp9/bzh6eGOnNz81vhX323FyGGD4ebq0mX5FhQW4/d/+w++WLepNfblB//GA0sWdNmcREREREREREREREREcpEkCXGJyUIsJjLM4PtkXc3Fq//9AM2aZsyZNQ3zZt0Fa2trE2V5ZyqVFR5feR/eXbMO6Vm5AID6+npkZGTg+PETCAzww9BBIVCpbvyZSm1d9+5Udzk9C2s3bEF5RaUQVygUeGjpAgwfHNKt+ZDxruYV4GhsIhJS0qBpbjb4+obGJjRqWoRYbl4Bps5ZgtBRw/GrZ5/AkoVzoFarTZKvJEnYuHUXfv3y33E1N084tu/gUazftB3L7p9nkrmIiIiIiIiIiIgsCZsbicisxemsUhodYXgh7/r1avzn/U9RWFyCaRPHYeGcmbCxsTFRhh0zfVI06hsasfvgcQBAS0sLiouL8a8330MfR3uMGj4Ujg72AICamtpuza2i6hq++W4r0i6l6x2bEDUGC+65q1vzIeNJkoRLmTk4eioZZy9moKVFe+eLbtHYpEF6dh6sVGLB+9O1G/DNxm14eMUi/OKJhzAwJNhkOTc0NOKNDz7D39/4ALW1YjH7hd+/itkzp8LVxdlk8xEREREREREREREREZmDjKwclFdUCbFoA3dulCQJix9+FmdSzgMAvly/GV6e7nhw2f14bNUSk9Z0bsfaWo1nHl6KN/73JQ4fj0N5eXnrsZy8ApSWlWPU8CHwcHeDj7dXt+TU0NiIrbsO4MiJU3rHVFZWeGTFQoSOGNItuZDxmjQaJKak4WhcIq7mFRp9Py8vL1hZWaGlRWxyTE49jwcefw6/+sPf8PxTj+JnDy2Hq4tLp+dJSjmHX/z2zzgWe7rdc37zp9cw754ZsLe36/Q8RERERERERERElkgpdwJERO1paGhE0tk0IdaZVUpf+P3f8Jd/vo2Pv1iH5Y89B/9hMXjxD3/DxcsZpkq1Q+bMmIR7pk1AYWEhUlNTkZ+fj+bmZlRWXcex2HhkZudAkiR4eXp0Sz6SJOHEqUT87V/vttnYOHX8WCxbcK/F7ZTZm9XVN+DgyUT85c01ePuz75B8/orBjY238vHx0YvV1zfg/U+/wpCoGViw8gkcPXlab7dPQ0iShO+37cHwcbPw+7/9R6+xEQDKKyrxl3+90+k5iIiIiIiIiIiIiIiIzJXuYq/eXh4I6utv0D0uXEpvbWy8qaS0HP965yMMHjsd0+avxLffb0dDQ6Ox6d5WRUUldu39AXU112BtpV9jrG9oRELyOaht7ODs3LWLWmq1WqSev4hX//N+m42NNtbWePrhpWxstBBarRY/HDuF37/2DtZu3NGhxkalUgk3F2d4e3ogwM8Hfj5e8HR3g4O9fes5tra2mDp1aru7nBYVl+C3r7yGgGFRePbXf0R6ZrZBeReXlOLx515C+JQ5t21sBG7sGvnvdz4y6P5EREREREREREQ9AXduJCKzlZR6HhqNpnWsUCgQGTbKoHs0NDTim++3CbHyikr8971P8d/3PsXEmEg8vnoZFs2/F3Z2tibJuy0ajQbxZ1JwIe08nB1skNfcLBzXarW4cDkDCis1VgX27bI8biooLMb32/fgQjsNnrOnT8K90yexsdFC1Dc0Ytv+YziZeBYaTfOdL/iRysoKarUaSqUSzS3N0GiaodX+1Aw5bNgwFBYWIjs7W+9aSZKwbc8P2LbnB0SEjsQLTz+CRfNmQa1Wd3j+lHMX8MLv/4bDx/ULyrre++QrPPHgcgwZFNLh+xMREREREREREREREZm72IQkYRwdHmpwje7wids3TR0+fgqHj5+Cm6sLVi1dgMdXLcWwIQMNzrU9Vdeu48jJeKScuwCtVgulQoGwkUPglleIi+lZaGnRwsrKCt7e3vD29kZ+USle/vsbGDV8CCaNi8TgAf1NVpesqa1F7OkkHIuLR1l5ZZvnDOjXFw8smgsvDzeTzEldq7i0HF9u2I6snLx2z1EqlfDz8UJQX3/09fdDYIAf/H292q1dVl27jpy8ghuPoYMwcsRwxCckIjU1FdevX9c7v66uHu9+/Dne++QLzL93Jl78+eMYHx3Z7vu2qakJb3+4Bn/551uorq5p85zJ46PR2NSIuPifvga8/tYHeOSBJQjw973dS0JERERERERERNSjsLmRiMxWbLxYyBs+ZCCcnPoYdI/TZ1JuuwLpsdh4HIuNx3O//TNWLV2Ix1cvw8jhpluds7m5GfFJZ3EiLh41tbUAgOC+fujj6IDUtMuoq28AALi6usLPzw/29vb47/ufITDAD5PHRyE8dASsDWgWu1Muyecu4OiJ00jPutrmOc5OfbDivtkYOdR0xUzqWucvZ+HrLXtRda263XMUCgV8vDzQN+BGIS8wwA8Bfj6wtbHRO/fa9Wrk5BUgN68AV/MK4OnhjivpGUhNTUV6enqbuzQmJJ/Fyp+9gJdeeR3P/uxBPL56KVycndrNp7SsHC+/9iY++XK90Ex5a74r7p+L77btRlPTjQbn5uZm/PpP/8D2bz/uyMtCRERERERERERERERkEU4lpAjjqIhQg+/R1s6EbamorMJb//scb/3vc4yPCsdjq5Zi8fx7YG9vZ/Cct97zs682oKauQYgrFAoE9/WDv683Kqvr0KxVQKX66U9UtFotks+mIflsGrw9PTAhJgJDB4bA28sDVlZWBuVQW1eHq7n5iD9zFokp59Dc3PZioGq1CvNnTcOUcZFQKrnIq7nTarU4ePw0tu89DE07H1NXF2dMiI7AuLFj4GzA3xK4ODvBxdkJo3782wBJknApPROHjsVh2849SE5ORlFRkd51kiRhy8692LJzLyLHjMYvn3kc98+7p7WJUpIk7NhzAL/8/V/b3eUxODAA//7r73HfvHtw9vxFhE26t7VmWldXj//78+tY+9GbHX4uRERERERERERElo7NjURktuISkoVxdESYwfc4fDyuQ+dVXbuOdz76Au989AWiI8PwsweXY8mC2XBwsDd4zpuam5vx9YbNba4g6e7qjInRY1Bd24hrtQ2wtxfnyckrwNr1m/H9tj2IGRuG0JHD0NfPB9bW1gbloNFokF9YgrNpF3HiVCKut7MqJABEh4/GojkzjCpeUvepb2jExl2HEJt4tt1zfL09MWncWESOGQV7u459XJ2d+mDksMEYOWxwa6yktBzH4xKw94cjOB0fj7S0NDQ1Neldm1dQhJdeeR1/+dc7eGTlYvziiQfRPziw9XhTUxPe+/Qr/OWf7+Da9babMSdER+DNv/8BY0aPQIC/L15/68PWYzv3HcLeg0dx97RJHXouRERERERERERERERE5qyurh4p5y8KsZjIUIPuIUkSDus0N/7hVz9HXX09vvh2E8or2t698MSpRJw4lYjnf/dXrFw8Hz97cDlGjxxm0NwAcOlKJmpq6wCFUohbq9WIighDzNhw2Nra4tipRGzZfQiNPy5seavi0jJ8v20PAECtViPAz6d1wU53NxdYq9VQqVSQJAlNGg0a6huQV1iEnLxC5OQVtPscbxUS3BerFnO3RktRVlGJNd9ubXe3xqGDQjBp3FiMGDrI4GbYtigUCgwZGIIhA0Ow7L45OHEqERs2b0dsbBwyMjLaXAA2/kwKlj/6c/QN8MNzTzyMiTFj8fKr/8b+Q8fanMPBwR6/++Uz+OUzj8HW1hYAMGrEUDy2ehk++vyb1vO+2rAZzzy+GtGRY4x+XkRERERERNQbKH989HZ8DYgsmUJq6zdwXaS4uBiTJuk3JBzZ9BG8Pd27K407U1jqF7ZeuLKgwpKfsxG5G/W8jXzNjPj8UBiYd+DICcjNL2wdf/LWa3h01VKD7jFt/kocOhbbOl5231z09ffF599+j9Ky8tte69SnD1YsmofHH1yBMaEjDZoXAPILivDxF9+0+fEaNKA/pkyIgZ+vD65k5uCr73egtPz2RTeFQgFfb08EBvihb4Af/Ly9YG2tbl0FUqPRoEmjQXFJGXLyCpCTV4CCopI2d8a7Vc/brdHIb2vS7V+v29K2dMu8F9KzsXbTnjZ3a1QqlQgdOQyTxkViYP9ggz/vbqdJo0Fi8jkcOHQMew8cRGpqKqqr298xUqlUYuHsGXjh6UdRde06fvn7V3E5I6vNcwMD/PD6Ky9hyYJ7W3Ourq7B4KgZKCoubT1v6KAQJB/d0fq+N44R7xVjf3wy5n1m7HvcGN33Y2MP0ls/XjJ+flmqXvm8e+NzJiIyI73yew8AyYh/t924gRHX6v/7rLi0ApOX/FwvfvToUXh7exsxFxEREcmp3Zrodx/A29OMmmpkrbNZaI3PomuTFsiY19vYmr8x1ys63mh1LDYek+eubB0rlUpcy04yaAHWtItXMGL8vUIs48wh9Avqi8bGRmzZdQAff7EOB2+pmbZnbPhoPL56OZbfP6/DOcSeTsTeg0dbXzOlUomYyDEYFxWhd4/yiirsPRKL+KRzbTY5dgV/Hy9MHR+J6PDRbe/WKNvntSV/PenaGkBeYTHe/fRbXK+p1Tvm6eGGVUsWYED/4M7n0EFNGg227/kBW3fuQ2pqKi5cuNDmArAdsXrZ/XjtTy/Bz1f/3/olpWUYGD4F129ZHDYqIgwn922CUmnk1zKjapIw6vdHkrFzG0Wm33sZ/fu23lhXNLbubsz1lvp6sw5rMEutmxNR+3rr1zOj8DXrVnL+LKxte9f7DjHmc6uNf1sXl1Zg8rLn9eKsg3Zeu793/ubf8PZwlSEj81JcVonJK36lF+d7jsgycOfGtpj4m3P3kfOHT5met5z/SDH6Y23ML5uMmFbOvA14n+QXFAmNjQAQY+DKhA0NjYiNPyPElt8/F/PunYG//eFFbN21Hx9/uQ77Dx1v8/rr1dX435qv8b81XyM8dCQef3A5lt8/H05OfTo0f1tNhSH9gjBt0nj4+/m2xgb2D8TvfvEYDp2Ix7G4M6i8dr3N+0mShIKiEhQUlejtatkZTo4OGD82DHdNioa9nS1M+zXEgotxsv3yu2PXnkg4i2+27GtzddAhg0KwctE8uLt1zT9SrNVqxESGISYyDPfPvxdr12/GmaRkpKamoqioSO98rVaL77fvxffb97Z7Tzs7W/z2+Sfxq2ceg52drXCsTx9HvPr7F/Hoc79tjV24nIH/rfkWz/5stemeWKfwFz5EREREZCIswFoWi/29IRERERERmaO4hBRhPGr4YIMaGwHg8InTwjgwwA/BgQEAABsbGyxdOBtLF85GemY2Plm7AZ9/+z1KStteBPZ0YgpOJ6bgb/9+B0d2rEfQj/e5HT8f/T8OmzIxps2FKt3dXLBi4T1YeM9UnDpzDkfjElFUcvsFaTtDZWWFsJFDMSkmHP0D/U26GCh1rcyreXhvzXrUNzQIcYVCgakTojHvnrtgbW3dLblYq9W4f+4shI4chrXrNyMiIgIXLlzA2bNnb7sA7K2iI8fgzdf+iKiIsHbP8fL0wB9//Rx+9fKrrbFTCUn4duNWrFyy0OjnQUREREREREREZO7Y3EhEZulUoljIc+rjiCGDQgy6x+kzKWhoaGwdKxQKTBw3FgBgbW2NxQtmY/GC2cjMzsGna9fjs6+/E3aIu1Vi8lkkJp/Fy6/+B9vXfXbb4sNNPt6eUCgUQgvUlAnjhMbGm2ysrTFr6njMmBSDcxev4NipM0i7nGnQ8+2oAf0CMSkmHKHDh0Cl6vjKsSS/H44n4Pvdh/XitjY2uG/u3RgfFd5txdlhgwfgj795Fpt37MPxkBAUFRUhNTUVmZmZbTZetmXlonl47Y+/RoC//ufETQ8uvw/vfboWZ1LOt8Zeef0trFg0t8uaOImIiIiIiIiIiIiIiLpDXEKSMI4KDzX4HkdOnBLGU8ZHtVkvGtA/GP/402/wl/97Htv3HMQna9dj36HjbdZ1rubm46MvvsWrL//6jvP7+ngJNVGtVovikrLb1n/sbG0xZVwEJseE40pmDk7EJyMjOxcVVW0vAtsRKpUVAny9MXr4YIyLGI0+jg6dvhfJIyevEO9+9i0aGsXdEbtzt8a2hAQH4ne/fBrb9/wAGxsbjBw5EllZWUhJSUFxcXGb1/j5euOff/4dli+a3/aOoTqefeIhfPj5N7iSkdUae+mVf2DB7LsNbngmIiIiIiIiIiKyNGxuJCKzpLvjYlR4KJRKpUH3OHw8ThiHjhwGVxdnvfP6Bwfi1Zd/jVd++zx27juEj79Yh90HDrdZyCstK8df//UWdqz//I7zq9VqeHl6oLi0rDVWUFSEvgF+7V5jZaXE6OGDMXr4YJSUVeD46SSkXc5AYXFZhxvGdCkUgJeHO4YM6IcJUWPg7+vVqfuQvNprbBwyKAQPLJ4PN1eXbs/JztYWKxbNQ9ioYfhqw1b4+Pjg+vXrOHfuHNLS0qDRaNq8LjJsFN587Q8d2o1VqVTijVf/gMlzlrfGKquu4ZXX38Y7r//JZM+FiIiIiIiIiIiIiIioO0mShNj4ZCEWHRFq8D0O6zQ3Th4fddtrrK2tcf+8Wbh/3ixk5+Th06++w5qvN6KgSGzSsrLq2CKp1tbW8HBzQ2lFZWusoKjots2NNykUCgwKCcKgkGAAQHVNLXLyi5CTX4ic/CLkFRajvr4BmuZmaDTNUCqVsFaroFKp4OHmgr7+Pgj090Wgvw/8vD07nDOZn6KSMry7Zp1eY+OAfkF46pGVsLOzlSmzG27u4tg/OBBrvvoOISEhCGljAVhbWxv8+tkn8dLzT93SlHjnOr+1tTX+87c/YN7yR1tj+QVF+Odb/8Off/fLLnpWRERERERERERE5oHNjURkluISkoWxoYU8AHqFvCkTom97vlqtxoLZM7Fg9kzk5Objs6+/w6drNyCvoFA4r6VF2+EcfL29xObGwrZXbmyLl4cb7ps9HffNno6mJg3yCotxNa8QuT8W865dr0ZzczOaNM0AJKhVaqjVKvRxdBAKeX39fWBrY9Phecn8nEw422Zj4913TcK8WXd1226N7Rk6aAB+/+LT+OCzr5GRlYNx48YhPDwcFy9exLmzZ3G9uhrAjc+Hv7/8K6xausCgZuWJMZFYsuBebNiyqzX2vzXf4MmHl2P4kEEmfz5ERERERERERERERERdLTe/EEUlpUIsJjLMoHtcuJSO0rIKITZlwu2bG28VHBiAv/7uBbz4zKNwDwkXFludGBPZ4fv4+XqJzY0G1ERv1cfRAcMHh2D44BC9Y5IkyV4To67R0NiI9z5fj5raOiE+fMhAPL56KaytrWXKTF/YyGGwfXQlPlzzLZo0Gvj4+MDHxwc1NTUY0j8Ajz2wGF6eHp2695xZd2HG1InYf+hYa+yfb/8Pj65aisC+/qZ6CkRERERERNTTKBQ3Hr0dXwMii8bmRiIyOxqNBgnJZ4VYdGSoQfdoaGjU2/1xyh1WKb1VYF9/vPLb5/Hyr5/F4Mi7kJF1tfXY+KiIDt/Hz9cbyefSWse6K552lLW1Gv2DAtA/KEDv2M0iI4t5PdOljBx8vWWfXnzh7JmYMXWCDBm1zd7ODs8+vhoffbEOaZfSYWNjg9GjR2PkyJG4XlWJ2dNiMG/WNDg6OnTq/q+/8hK27fkBDQ2NAICWlha8+Ie/Y/d3a/jeJyIiIiIiIiIiIiIii6O72KurizMG/riDYUcdOi4u9hoY4IfgQP164p1zSRIaG9VqNcaNDe/w9X4+3kg5f6l1XFhUYnAOd8J6UM+1efdBlFdUCbHBA/rhZw8ug1qtliep2xg6aACeeHg53v/0a7S0tAAAHB0dUVxRAwmdf58qFAr899WXMXrCLGi1NxZcbmhoxG///A9888k7JsmdiIiIiIiIiIjIHHV82yQiom6Sev5iawPTTVHhoQbd4/SZFOEeCoUCkwxobrypsuqa0NgIAFMnxnT4ej8fb2FcVl6BpqYmg/O4HYVCwWJeD1Xf0Ii1m3YLxWQAWDB7hlk1Nt5kbW2NJx5ajsED+7fGlEolXNzc0axQd7qxEQCC+vrjxWceE2L7Dh3Hrv2HO31PIiIiIiIiIiIiIiIiueg2N0ZHjDa45nfkhNjcOGV8VKfqhoeOifcZO2Y0HBzsO3y9bk20pKwcGo3G4Dyo97mUno1jceKixUF9/fHEQyvMsrHxpqGDBuCRlYuEmKa5GV99v6u1MdEQjY2N2L77AP7x5vtQqcR16r/duA2Z2TlG5UtERERERERERGTO2NxIRGZHt5A3aEA/uLu5GnSPw8fjhHHYqOFwcXYyOJejJ08LY3t7O0SOGd3h6729PKBU/vSlVpKkLlmplHqmzXuOoqKqWojNnDYRM6dOlCmjO1Or1XjioeV6qwKfSjqPc5cyO3XPurp6bNy2Gym37IJ6059ff7tT9yQiIiIiIiIiIiIiIpKTbk3U0MVeJUnCYZ3mxsmdWOwVaKNJckK0Qdf7eHsKTZWSJKGouLRTuVDv0dDYiLUbdwgxa7Uajz6wGLa2NjJl1XFho4brfa5kXM3DoZMJHbq+qakJO/f+gAef/CW8BoZj3vJH8fWGLW0ulpyemW2KlImIiIiIiIiIiMwSmxuJyOzExicJ4+iIUIPvoVvImzKhc4W8Q8dihfH4qAhYW1t3+Hq1Wg0vDw8hVlBU3KlcqHe5mH4Vx+NThNjAkGDMm3WXTBl1nK2NDR5bvVSv6PjNlv2oq2/o0D3q6xuwafteLH/sF/AaPBZLHn4WO/Ye0jvv/KUrnVr9lIiIiIiIiIiIiIiISC6NjU04k3peiBlaE027lI6y8koh1pma6PXr1UhMOSfEpk6MMegearUanu5uQow1UbqTzbsPoqLqmhBbMHsmPHTeS+Zs/j3T9d772/YeQXFpeZvnazQa7DlwGI888yt4DwzHnKWP4Mt13+P69eo2zweA4UMHdbpxmYiIiIiIiIiIyBKwuZGIzE5cgm5zY5hB1zc0NCI2/owQmzLesNVFbzp8XGySNLSQBwB+vt7CuKCwqFO5UO9R39CIrzbvEWI21tZYtWSBsBOoOXNzccb9c2cJsWvVNdi463C71zQ0NGLrrv144IlfwntwFBY99AzWb96Jurr6dq95wIJeEyIiIiIiIiIiIiIiIgBIPncBTU2a1rFCoUBU+GiD7qFbxwwM8ENwYIDBuRw/lYiWlpbWsbW1NWIixxh8H9ZEyRA5+YU4FifW9AeGBGPSuEiZMuocGxtrPLBkgRDTNDdj444DrePm5mbsP3QMjz/3EnwGReCeRQ9izdffoera9Xbvq1AoMHl8NN77918Rf3A7bGzMfydLIiIiIiIiIiKizlLJnQAR0a1Ky8qRkZUjxAxdpfT0mRQ0NDS2jhUKBSaOG9upXM5duCTEpkzoRHOjjzfOpJxtHRcWlxh8D+pddh2KRUWVuDrngtkzLGqVUgAYN3YMklLPI+1SemvsVNJ5xIwZjoH9+gIAGhsbsf/wCXy3ZRe27j6A69U1d7yvlZUVpk8eh5WL52Pl4vldlj8REREREREREREREVFXiEtIFsZDB4XA2amPQfc4ckJnkdYJUVAoFAbncuh4nDCOCg+Fvb2dwffx8/VG8tm01jF3bqTbOXIyQRhbq9UWtdDrrQaGBGPKhGgcvuVz6ezFdHy/fQ/2HzyC77ftRll5RYfuNSEmEksXzsH98+6Br4/3nS8gIiIiIiIiUihvPHo7vgZEFo3NjURkVk4lpghje3s7jBw22KB7HDoWK4zDRg2Hi7OTwbkc1inkOTjYIyJslMH30V2ltKy8Ao2NjVxdkdrU0NiEE/GpQmxgSDAmxljWKqXAjcbiFYvn42//ehcNjT81HO8/dhrpGZn4bssubN65D9euV9/mLjcolUpMmxiDxQvuwcLZMy2u0ZOIiIiIiIiIiIiIiOimUzrNjVEGLvYqSRIO6zQ3Th4f1alcDh8Ta6JTJ0Z36j5+Pj7CuKy8Ek1NTbC2tu7U/ajnqq2rR0JKmhCbOW2iRdf/5t8zHacSkpCRmYX09HRkZmbiww8/7NC146LCsWTBHCyafy/8/XzufAEREREREREREVEPw+ZGIjIrcQlJwjgybBRUKsO+VOkW8qZ0tpCn09w4IToSarXa4Pt4eXrAysoKLS0trbHCohIEB/XtVF7UsyWkXEBDY1PrWKFQYOWi+Ra5SikAuLk4Y87dU7F+804UFBQgPT0dn32WhcZbmh3bo1QqMXncWCxZOBsLZ8+El6d7N2RMRERERERERERE1EtIUuev7cTucDqTG3GtsXMbwZjXzBhGv95kbmJ1mhujw0cbdH3apXSUlVcKsSkTDK+JVl27jqSzYpPZlAkxnXrPeXt5QKlUQqvVArjRgFlYVIKgwACD72UQfn50gpFfy4z8WhiXmApNc3Pr2MrKChOiI4zLSSZarRYn405jw6at+GrdRlRWVnXouqiIMCxZMBuL5t+LwL7+tz9Zru89NyaXcW4iM2b05yU/t4iIqLsZ8+8mft8ymJy7xxk1t9ZkaRARERnKPJobJUnmX8bp6I0/w8lagDSGBRcvZSuyGPux6nzeUgdes9jTYnNjVPjo1us68pI1NDQiNv6MEOtMIQ8ADh3XXaV0XKc+biq1Gt5enigoLG6NFRQXIzg4sFN5GcZSi3lGvE/l/CWyEXNLkgRJknDkVLIQHz5koMU29TU3N+PIsZP4esP3+HbDJjQ0NNzxGoVCgYkxEViyYDbum3M3fLw9uyFT6jBjvneZ0896RERERLr4s0ovYuwfcBpR1JOzkElERERERGajsKgEV3PzhVi0gTs3Hj4uLvYa1NcfwZ1oIjwWG9/ajAgANjY2iBk7xuD7AIBarYaXhzuKSkpbYwVFxV3f3EgWRauVcDQuUYiFjRoGpz6OMmXUeZlZ2Vj+0BM4nXDmzicDiAgbhSULZ2Px/NlcCJmIiIiIiIiIiOgW5tHcSEQEoKWlBafPpAqx6Igwg+5xKjEZjTq73k2MiTQ4l+KSUly4lC7EpkyMMfg+N/n6eInNjbf8P9FNWbkFyC8qFWKTx42VKRvj/HDoKB556hfIyc3r0Pnjo8KxZMFs3D/3bvj5endxdkRERERERERERERERPI4dSZFGPdxdMCwwQMMuseRE2Jz45TxnasnHda5T0xkGGxtbTt1LwDw8/UWmhsLi0o6fS/qmS6mZ6FUZ9fRSRZYD92xex9WPfY0qqqu3fY8P19fPPuz1ViycA76d8vix0RERERERERERJaHzY1EZDbSLqWjprZWiBm7SmnYqGFwcXYyOBfd+/Tp44jw0JEG3+cmPx8fJOJs65jNjdSWo6fEYra7myuGGljMlptWq8U//vMWXv7LP4SVftsycvgQPLzifiyaOwsB/r7dlCEREREREREREREREZF84hKShfHYMaNgZWXV4eslSdJrSpw8PqpTuRw+HieMp0zo/GKvAODn440zKedaxwVFrImS6OyFy8LYz9cbIRbU9NfS0oI//e11vPrPN9o9x93dHSEhIQgJCYGLiwuefHQVXJz6dGOWREREREREREREloXNjURkNuISkoRxcGAAfLw9DbrH4RM6Bbjx0Z3K5dCxWGE8MWYsVKrOf8nU3YmuorIK9fX1sLOz6/Q9qWfRarVIuSDuFjopJhJKpVKmjAxXWVmF1Y8/gx2797V7jpeXV2sxb1JMOB5bNrcbMyQiIiIiIiIiIiIiIpLXqQRxscsoAxd7TbuUjjKdne+mTDC8ubGy6hqSz14QYlMnGtncqFMTLSuvQGNjI2xsbIy6L/UcV/MKhXFE6AgoFAqZsjFMaWkZVjz8JA4cOqJ3rF9wEB5etRz3z5+DT77aiIbGxtZjOflFbG4kIiIiIiKiLmYZ/7YmImoPmxuJyGzorlJq6K6NDQ2NiI0XGyQ7U8gD2miSNHKVUi9PD6hUVmhubmmNFRaVoH+/IKPuSz1HUWkFmpo0Qmxs+GiZsjFcUnIq7l/5CLKyr+odGz1yOFYsuR/B/frh8InTrfGcfK7WS0REREREREREREREvUdzczPik88KsejwUIPucfi4uGtjUF9/BAcGGJzL0ZOnIUlS69jW1sbgRktdXp4esLJSoqVF2xorLCpBcFBfo+5LPUNLixb5hSVCLDDAX6ZsDBN3OgGLH3gUefkFeseWLlqAT957A46OjgCAAH8fpGf+VDPNyS/CqKEDuy1XIiIiIiIiIiIiS2M520ERUY8Xp9OYaGjx7FRiMhobm1rHSqUSE2MiDc6joLAYl65kCjFjVym1srKCt5e4C2VBERu76Ce6jX6uLs5wtpAVPNd8+Q3G3TVbr7FRqVTitT//AWdOHsRvfvksxkVFCMfLK6+htq6+O1MlIiIiIiIiIiIiIiKSzdm0y6jTqY1ERxi22OXh4zqLtI4f26lcDuk0SY4bG270DosqlQrenqyJUtsKS0qhaW4WYoEBfjJl0zGSJOG9Dz/FpJnz9BobVSoV3vrXq/j2849aGxsBIEinYTMnr6hbciUiIiIiIiIiIrJU3LmRiMxC1bXruHA5Q4gZu0pp2KhhcHF2MjiXIyfE+zg59UHoqOEG30eXn4838gt+KlwUspBHt8gpEN8P5l7IA4CGhgY8++L/4ZPPv9I75unhgXVffIRpUya2xny9PaFSqdB8S9Eyt6AEQwZwB1MiIiIiIiIiIiIiIur54hKShfGAfkHwcHfr8PVarRZHTp4WYpPHR3UqF92aqLGLvd7k6+MlNDQWFLImSjfk5BUKY3dXFzg62MuUzZ3V1tbiiWd/ha/Xb9Q75ufrgw1rP8H4GP3PP906b04BmxuJiIiIiIiIiIhuhzs3EpFZOH0mRRhbW6sROnKoQfc4fEJ3ldLOFfIOHY8VxpPGRUGlMr4X3M/XRxizkEe3ytVrbvSVKZOOycq+ivF3zW6zsTEmKhJJsQeFxkbgxg6mAb7eQky3qZOIiIiIiIiIiIiIiKinOpWYLIwN3bUx7VI6ysorhdiUCYbXRMsrKpFy7oLOfUzT3OjnI9aCuHMj3ZSTLzb5mfNir5cupyNq8qw2GxunTpqAMyd+aLOxEdB/Xtera1F1vbpL8iQiIiIiIiIiIuoJ2NxIRGZBd5XS8NEjYGNj0+HrGxoaERufJMSmTIjuVC6Hj+s0SXbyPrr8dJq6Kquuoa6u3iT3Jsum1WqRV1gixAL9zbeYt2vPfoRPmI4zyal6x5576nEc3rMF/n5tN2f21VuplAVtIiIiIiIiIiIiIiLqHeISxAVfx4Yb1tx4+Li422JwYACCAwMMzuPoyXhhbGdna3Au7dGtiVZUVqG+njVRAiqqrgljf533irn4fst2RE6agfMXLuode+mXz2Lf9u/g7e3V7vWeHm5Q6yyeXFl13eR5EhEREREREd2gABR8AAq5PxBEZAQ2NxKRWTil09wYFRFq2PWJyWhsbGodK5VKTIiOMDiP/IIiXMnIFmJTJ44z+D5t8fRwh0plJcQKuVIp4cZqnU2aZiEW4G9+Oze2tLTgj3/9B2bfvwKVlVXCMQcHe3z7+Ud4699/h7W1dbv36KvzvMoqqto+kYiIiIiIiIiIiIiIqAcpr6jE5YwsIfbSn/+FKXNX4rd/+Re27f4BpWXlt73HkRNic+OU8WM7lcshncVex48Nv219xxBenh5t1ERL2jmbehNNs1gPtbXt+GLH3UGj0eBX//cnLFr5CKqra4RjTk59sHndF/jHX/8IlU7joi6lUgkbG/HzSbcWTERERERERERERD+5/W/ciIi6gVar1du58ZuN25CXX4iYsWMQExGGsFHDYGtr2+49dFcpDRs1DC7OTgbncuhYrDB2cXbC6JHDDL5PW5RKJXx9vJGbV9AaKygqRkj/YJPcnyxXU7N+Mcverv33uxzKysqx4uEnsf/gYb1jQwYPxPdfr8GwoYPveB/d56VhIY+IiIiIiIiIiIiIiHqB3PxCvVh9fQOOxsbjaOxPOymG9AtETEQYoiPDMG5sGEYMHQSVSgWtVosjJ08L108eH9WpXPSaJCd07j5tsbKygrenJ/ILi1pjBUXF6N8vyGRzkGVq1qkLqlRqmTLRV1hYhKUPPo5jJ+L0jo0aMRzff/MZBoT07/D9dBsgm9uoBxMREREREREREdENbG4kItlVVl1DZdU1IVZSWo6N2/Zg47Y9AABrazXCR49ATOQYxESGISYyDP5+Pq3nH9YpMkzpZCFP9z6Txo2FlZVVO2cbztfbS2xuvKWoR71XWw1+d1rxszudik/E4gceRW5evt6xxffNw6fvv4U+fRw7dC/d58XmRiIiIiIiIiIiIuo9pB8f5kLR+UslI5+Hwoi5jXoNjZlXRrK+3mS4tj9eIcF94evticLi0ttenZGVg4ysHHz13VYAgIODPcaGjcLggf1RVl4pnDt5XKT4/ujAx7q0rBxn0y4JsSkTou94nSH8fL2F5kbu3NgWc/p+0D20Ol/LlErz+Np09PhJLF39OIqK9d+nq1cswQdv/Qv29vYG3VOpVApjrdaYj7cR1xr7/YOIiIioPfw5g6jnUSjvfI6c9yMioh7NPDonJEn/B105CyzG/NAt6+9eZSxAGkO24qWxjPxgy/Y+M48Cwa2c+jgiqK8/rubqN07d1NSkQWx8EmLjk1pjgQF+iIkMQ3REmBAHOl+AO3RMbG6cOjGmU/cR3PKx9vPxxq3v24LC4tu/F1hsNpykNfL67i8sabUtwlipVEJhBh97SZLwwcdr8Pxv/gCNRiMcU6lU+Nerf8IvnnnCoFytdAp5LVpjPl5Gfg+w2F+yWer3TTKYrO9RvleIiCyaxf6cQ9RRMr7HTf5vRn6+EhERERH1Fn36OOLQ1rX45zuf4FhsAq5kZnfoutraOhw6HodDx8U6ZnBgAIIDAwzOQ3f3RwcHe0SGjTT4Prdzoyb6k4KiYpPenyyT3iKoMu9mKEkS/vv2B3jp5b+gpUWs11pbW+Od/7yGxx9e1am6re5OjWq1efx5FhERERERERERkTnib8+ISHZqtRrbv/0Yf3rtTRw6Hoeqa9c7dF1OXgFy8gqwfvNOIa5UKjEhOsLgPHJy85GZnSPEumKV0ltdu34dtbV1cHAwbKVH6ll0C3larRYtLS0m3TXUULW1tXjyuV/jq3Xf6R3z9fHGhrWfYMI4wz8/dIuULOQREREREREREREREVFvMWhAP3zy1qsAgLLyCsQlpCA2IRmxp8/gdNJZ1NXVd/heU8aP7VQOh4+fEsYTosKhVqs7da/26NZEK6uuoa6uHvb2diadhyyLtU5dUNOkaefMrnf9ejUeeeo5fL9lh96xwL4B2Pj1Z4gMD+v0/Zt0Fo5Vq1gTJSIiIiIiIiIiag9/e0ZEZmHE0EH4/sv3odVqcelKJmITbuzSGBefhPMXrxh0r7BRw+Di7GRwDodPiKuduro4Y9TwIQbf53Y83N2gVqmhaf6pmFFQVISBIf1NOg9ZFrVKv4lR09wsW3Pj5SsZuH/FwziXdkHv2JRJ4/Htmg/ho7PibkfpNTeykEdERERERERERERERL2Qh7sb5tw9FXPungrgxk5vZ9OuIDbhRo00NiEZGVk57V5/97SJnZr3sM4OkJPHR3XqPrfj6eEOlcoKzc0/7Ya3YfN2LL1vLuzs2ODYW9nrfOxLyytkyePc+Qu4f+XDuHwlQ+/Y3dOn4uvP/gd3d7dO37+6phYNDY1CzN7OttP3IyIiIiIiIrothfLGo7fja0Bk0dhRQERmRalUYujgARg6eAAeWbkYAFB17TpOJSYjLj75RsNjQhKuV9e0e4/pk8d3am7dVUonj4+CUmnaH3SUSiV8fbyQk5ffGjt45CS8PT3h5NTHpHOR5WirmFVReQ1+Pl7dnsumrTvw0BPPorqNz7HfvPAsXn3ld3o7TRqiorJKGNvZ2nT6XkRERERERERERERERD2FSqVC2KhhCBs1DE8/shIAUFJajriEZJyMP4O4+GTEJ59FfX0DFs2bhUXzZhk8R0lpOdIupQuxqROiTZL/rZRKJYL6BiAj62prLDsnD598uQ4rFi+Au5uryeck8xfg54345HOt45y8gm7PYcv2XVj5yFOoq6sT4gqFAn/8v1/h5d++aPQCtLrPS61Wwcuj882SREREREREREREPR2bG4nI7Lk4O+HuaZMw667JAACtVou0i1cQG5/U+rj446qKIf0C8fyTD3dqnkPHYoXx1ImmL+QBQGCAv9DcWFhcjI+/+AbLF82Hn69Pl8xJ5s3ezhYuzn1Qda26NZaTV9CtzY2SJOG3L/8V/3zjHb1jTk598PmH72DhvNlGz5OTVyiM/X08jL4nERERERERERERERFRT+Tl6Y5599yFeffcBeDG7o5NTRrY23du98PDJ8RdGx0dHDBm9HCj82zL9CkTkJObD01zc2usvKISH3/+DZYsnIP+/YK6ZF4yX4H+Yi28oKgEmuZmqI1YWNUQVVXXsPyhJ9DQ0CDE3dxc8dUn7+Oeu6ebZB7d5sYAX29YWXH3CCIiIiIiIiIiovbwt2dEZHGUSiVGDBuMxx9chs/efR0XTu1DeUYizp/cg3Mn9sDH29Pge2bn5CE7J0+ITZ0QY6qUBTFjw2FvJxYcq2tqsOarDUi7eLlL5iTzF+TnLYy7e6XSz9d+22Zj44hhQ5FwbL9JGhsB/efVV+d5ExERERERERERERERUdtUKlWnGxsB4PDxU8J4YkwE1Gq1sWm1ydfHGw8/sAR9HB2EeENjI77asBkJZ1K6ZF4yX7rNjS0tLSgsKum2+eMTk/QaG8PDRiPx+AGTNTYC+vXQQH/WQ4mIiIiIiIiIiG6HzY1E1CO4ubpg2JCBsLW16dT1h4+Lq5S6u7li+NBBpkhNj4ODPR5ZtQzubq5CXNOswYbN23H0RBwkSeqSucl86Ra1bt3dszvs/eGQXuyBZYsRd3g3Bg4IMckcNbV1KK+oFGJB/tytlIiIiIiIiIiIiIiIqDvoNjdOmRDdpfP5+frg8QdXwNfbS4hrtVrs2PsD9hw4BK1W26U5kPmws7WFl4ebELua23010SsZmcJ4yOCBOH5gB4KDAk06T65ecyProURERERERERERLfD5kYiIgCHjsUK4ykToqBUdt2XSA93Nzy2ejn6tVEoOXj0BDZt24Xm5uYum5/MT6DODoZ5+UXdWsy9ki4W8379/M/x5SfvwcHBoZ0rDJebLxby1CoVfDzdTXZ/IiIiIiIiIiIiIiIialthUQkuXskQYlPGR3X5vE5OffDwA0swdNAAvWNx8Un4duNWvd30qOfSbfRLu5TebXPr1kNDR46Ara2tSecoKilFRdU1IRbk72vSOYiIiIiIiIiIiHoaNjcSUa8nSZLezo1TxnftKqUAYGdnhweW3ofw0FF6x86mXcTnX29ATU1tl+dB5kF358YmjQZZOXndMrckSbicLhazZ941BQqFwqTzXE7PEsb+Pp6wsuKPIkRERERERERERERERF3tyMnTwtipjyPCRg3rlrmtra2x5L65mBgzVu/YlYwsfLp2HSorq7olF5LXwP5Bwvhs2iVUXbveLXPr1kMHDQwx+RxHT8YLY0cHO3hzsVciIiIiIiLqSgoFHzcfRGSx2FFARL1e1tVc5OSJO8pNnRjTLXNbWVlhzqzpmDVdv5Esr6AQH3/+NYpLSrslF5JXH0cHeLm7CrHjsQndMndxcYleI62pi3ma5macPH1GiPUP8jPpHERERERERERERERERNS2QzqLvU6MiYRKpeq2+RUKBe6aMgEL58zSW/yytKwcH3/xDXJy87stH5JHxOhhsLFWt461Wi2Ox3VPTfRKhrhz48CQ/ia9f2NjE+ISkoTYuIjRXOyViIiIiIiIiIjoDvgbNCLq9XR3bfT0cMewIQO7bX6FQoHoyHAsX7QANtY2wrFr16vx6Zff4tKV9G7Lh+QTEz5CGCemnENNbdfv3qm7SqmtrS0C/E3beJh8Ng3VOg2UMWNGtHM2ERERERERERERERERmdKR46eE8ZQJUbLkMXrkMDy4fDEc7O2FeF19Pb749juknkuTJS/qHna2togME2uEJ04loqWlpUvn1Wg0yMy6KsQGDjBtc2N8UioaGhpbxwoFMDEqzKRzEBERERERERER9URsbiSiXu/QsVhhPGVClN4uit1h0ID+eGTVUrg4OwvxpiYN1m3chpOn4iFJUrfnRd1nXPgIqKysWsfNzc2IPZ10mytMQ3eV0gH9+0GpNO2PCEdPnhbGA4MD4OftYdI5iIiIiIiIiIiIiMhCSFLnH8ZNLONDRrK93jLmbexDLgqFUQ9FO4/CohJczsgSppo2MUY4pzsF9vXH4w8uh5eHuxBvaWnBlp17UVRc0q35dJ7CiIcFM+Z9CmBSVLhwu2vXq5Fy7mKXppx9NUevgdKUOzdKkqRXDx0+eADcXV1MNgd1E0v83iE7C/35iKhb9NKfFaj79MZ/M5EMjPla1lsfREREZCg2NxJRryZJEg7p7Nw4dUKMTNkA3l6eePzBFeirs2ueJEnY98NRnEvr2qIOyauPowPCRgwSYkdj46HVart03ivpYnOjqVcpzSsoQkZWjhCbGBVq0jmIiIiIiIiIiIiIiIiobYdPiLs2ujg7YfSIoTJl82MOLs54dPUy+Pl4C3GtVouCwmKZsqLuEODnjZCgACG2c98haJqbu2xO3Xqou7sb3NxcTXb/pNTzyCsoEmKTo8eY7P5EREREREREREQ9mfk2N3JFkE6w0JWwLHb1GGNebyPnlvM162GfmxlZV5GvU2SYMiFapmxucHCwx4MrFmP4kMF6xy7rFF3I3Bi/as+kqDDhjuUVlTidmNKlWeu+r0y5SikA7N5/WBg7OTogdNhAE6wCbOTrbdSqsnLObaGMXG1aXhb6MxZZFov9/CCidvHfXkS3IePvVIiIiIiIiHqhQ8dihfGkcWNhZWUlUzY/qay6htKyciGmUqkQ0i9Ipoyou0yKEXdvLCwu0aspmtLl9AxhbMp6aHVNLdZt2iHEPNxcMHSgaWuuRERERERERG0zwd+09pgHEVkq821uJCLqBrqFPG8vDwwZFCJTNj9RKBSoqa3ViwcG+MuQDXWn/oF+CPDxFGLfbduNqmvXu2xO3ZVKB5mw0JaYfA5JZ9OE2PiIkVCp5C+YExERERERERERERER9QaHjscJ4ynjo2TK5Cf19fVYv2m73m59c+6eDmdnJ5myou4yZuQwBPiKu3buO3QcV3Pzu2S+Kxk69dABpquHrt+0AzW1dUJs3szJUCr5R5VEREREREREREQdweZGIurVDh8/JYynjI82wW5yxtt/6Ciu5uYJsf7BgQgPGyVTRtRdFAoF5kyfIMTq6xvwzcZtkLpgFx6tVov0zCwhZqqVSqura7B+s7hKqYO9HaaMG2OS+xMREREREREREREREdHt5eYVICMrR4hNnRgtUzY3SJKETdv3oLLqmhCPHBOK0FHDZcqKupOVlRKrF8+FUvnTny1ptVp8uW6zXsOrKVy+IjY3DjRRc+OZlHM4k3peiI0ePgjho4aa5P5ERERERERERES9AZsbiajXkiQJh46LOzdOnRgjUzY/OXv+AuLizwgxZ6c+uH/+bKG4Qz3XqKEDEKFT8Dp34TJOn0kx+Vx5+QVoaGgQYoMGmGb30nWbd+qtUrp07l3o42BvkvsTERERERERERERERHR7R0+IS726urijFHDh8iUzQ1HjsfhSoa4+GaAny9mTZ8iT0IkiwA/b9w7TVz0tbC4BNt3/2DyufR3bjS+Hlp17TrWbdJZ6NXOFsvm320WCyoTERERERERERFZCnbJEFGvdSUjC4VFJUJM7lVKi0tKsW3XfiGmUllh6f3z4MCGsF5lydy74OQofsw3bNmFCp0VbI11JV0s5Dk6OsDb28vo+yYknUWSziqlocMHInzkYKPvTURERERERERERERERB1z+LjY3Dh5/FhZF1S9nJ6JwzoL0DrY22PJwrmwsrKSKSuSy91TxyHA11uIHThyAgePxbZzheEaGhqQk5snxAaGGLdzY01tLd7+8Au9hV6XzJsJ5z6ORt2biIiIiIiIiIiot2FzIxH1WoeOxQljXx8vDAzpJ1M2N4oq6zdtg6ZZI8TvnXkX/Hx9ZMqK5OJob4dl82cKsfr6Brzz0Reorqk12TyX0zOE8cCQ/kavJHo5PQtfrt8sxBzs7bBs3nSuUkpERERERERERERERL1SeUUllj/2AobF3INljz6PdZt24np1TZfPe+i4WBOdOkG+xV4rKiqxadtuIaZUKrF4wRw4OfWRKSuSk5WVFVYvnqvXcLtx626cOJVokjkyMrMhSZIQG2BEc2NdXT3e/XgtikpKhfjo4YMQMXpYp+9LRERERERERETUW7G5kYhk19LSgjc++Az3rXoKL73yT8TFJ0Gr1Xb5vId0VnucOiFGtsYrSZKwecceVFRWCfHwsJEYEzpSlpxIfqHDBiJi1FAhVlxShnc//hJ1dfUmmUN358ZBA0OMul/W1Vx8sOZrNDc3C/Glc++Ck6ODUfcmIiIiIiIiIiIiIiKyVH/7zwdYv2UXLl7JxIatu7HiZ7+E1+BozF72OD7+cgOKS8pMPufV3HxkXc0VYlNkam7UaDRYv2k7GhobhfiMqRMRHNRXlpzIPAT4eWPp/Lv14l9/t9UkOzheyRDrob4+3ujTyd0Vr12vxn8/+Aw5eQVC3NvTDSsX3sOFXomIiIiIiKj7KZR83HwQkcXiZzARyW7t+i148Q9/x5Zd+/Gvdz7CuFmL0XfEBDz9qz9i/6Hj0Gg0d76JgSRJwuET4iqlUyZEmXyejjp28hQuXRF30PP39cE9M6bJlBGZi6Vz74K3p5sQy80vxBsffGaS1Xzb2rmxsy5dycTbH32BxsYmIT4+YiTCRw7u9H2JiIiIiIiIiIiIiIgs3a79R/RiTU0a7D5wFE/88mX4DZ+ASbOX47/vf6bXkNhZh3V2bXR3c8WIoYNMcm9DSJKEbbv2o7hUbOAcMXQwoiPHdHs+ZH4mRo3BvLun6MU3bt2Njdt2G/U3A5d16vADB3SuHpqdk4//vvcpCgqLhbibixOefWQ5HB3sO50jERERERERERFRb8bmRiKS3cZtu/VihcUl+N+ab3D3oofgPTgKq598EZu270VtbZ1J5rx4OUNv9dOpE2NMcm9DpWdk4dCxk0LM3s4OSxbOhUqlkiUnMh8O9nZ49qHFcHNxEuL5hcX473ufIq+gyKj7665UOmiA4Ts3SpKEU4nJeO/Tr/QaG8OGD8Ty+TO4SikREREREREREREREfVaVdeu40pm9m3PkSQJx+MS8KuXX0PImGkImzwXf/7nO0g9fxGSJHVq3kM6zY1TxkdBqez+PxNJPXcBZ9MuCjF3N1fMu5c1JPrJ3VPGYdbU8Xrxg0dj8fc3Puh006+x9VBNczO27tqPf7/7MUrLK4RjLk598Owjy/RquURERERERERERNRxbG4kIllJkoT4pNTbnlN17Tq++m4r7n/waXgOisSCB57AF99+j4rKqk7Pq7tKqb+fD0L6BXX6fp1VU1OL77ftEgqSCoUCixfMgbMzCyB0g5uLE557eAmc+zgI8ZKycrz+1ofYtf8wWlpaDL5vc3MzMrOuCjFDVyq9dr0aH37+Lb74dhOam5uFY8MH9cNDS2bLUiQnIiIiIiIiIiIiIiIyF4kp54WxUqmEWq2+7TUp5y7iz6+/jdBJczEocjp+9cd/4MSpRGi12g7Pe/j4KWE8ZUJUx5M2oZRzaXqx8opKbNi8A1cysjrdvEk9i0KhwLy7p2DBrKl6x4pLyvDvdz/Bph17Dd7FUbe5cWBIx+uh2Tn5+McbH2DvwWN6n3uebi548ckH4O3pblA+REREREREREREJOKWYEQkq9z8QpSWVdz5xB/V1zdg664D2LrrAKysrDB5/FgsnD0TC+6dgQB/3w7f59DxWGE8dUK0LKuCpp6/gPqGBr14QlIqVCoVAgL8uFopAQC8PFzx4s9W4O01G1BWca013tLSgh17DyLl3AWsWroQAX4+Hb5n9tUcvYbEjhbzbjYmb9i8C3X19XrHw0YMwsOLZ0OlsupwPkRERERERERERERERD1RfNJZYRw+ejj2fb8Gu/YfwZZd+7HrwFHU1ta1e31GVg7++96n+O97n8LH2xPz7rkLC2fPxNQJUbC2tm7zmqyrubiamy/Epk6INv7JdIKHuxsys3P04umZ2UjPzIa7myuiIsIwesQw2NjYyJAhmZOZU8bBwd4O67fuRfMtC7xKkoQDh0/g7PlLuHvaRIwJHQHrOzQJA8DlKxnCuCOLvRaVlOLw8VM4FhvfZvNtoL8Pnlq9GM5Ojh14RkRERERERERERHQ7bG4kIlnFnxF3bXR1ccaJ3RuwZdd+bNm5H6fPpLR7bUtLCw4ejcXBo7F49qU/I3LMKCycPRMLZ8/EkEEh7V4nSZLeKqVTJ8YY90Q6ybaN4pwkSTh/8RLOX7wEP18fREWEYfjQwVCp+CW7t/Nwc8EvH1+B97/YiLyiUuFYbn4hXn/rQ9w1eRymjI+CSwd2/rySLq5S6ubmCnd3t9teI0kSrubmY/eBIzibdqnNcyZEjsKyedO5YyMRERERERERERERUStjdqbjQpiWLjH5nDCOCBsJZ6c+WH7/HCy/fw4aGhpx4MhJbN51ANv3/ICy8sp271VUXIqPPl+Hjz5fB2enPpg9cwoWzJ6BWdMmoU+fPq3n6dZDPT3cMGzIQNM+MUH779MZUyehqUmD1PMX2tx5sryiCrv2HcIPR04gbNQIjA0PhZubaxfmSrLp4MK+46PGoF9QX3z53Tbk5BUKx4pLy/Dl+s34fvsexIwdg0kxkfBop8ZZU1ODwqJiITZoQNt/S9DS0oLU8xdx9ORpXErPavMcKysl7pk6HndPiYGVVQcXebXgnUm5qyoRmQ1+PSJzx/coEQn4exwiIiJD9cxOGWP/oWDMLmly/iPFqN3d5PzHlYw/xBnz8TJ6Nz0WMAEgIVlcpTQidASGDArBbweF4LfPP4m8/EJs3X0AW3bux+ETp9Byy8qMuuLPpCL+TCp+99d/Y8jAECycc6PRMSJspLD7YdrFKygtKxeunTIhBl32ut7mvTJq5DDk5BcgOfV8m8cLCouxefse7D90DBFhoxERNhqOjg5dk2dPYfTnpjHXd/33DxcnR/z6qQew6+BJ7Dt6WigotbS0YN/BYzhw+ARGDx+CSePGYtCAfu3u/nk5XWeV0tvs2tjU1ISE5HM4evI0cvIK2jzH0cEOy+fNQNiIQR16LkRkzuT8OYeISAeLgdQhfJ9Qd+F7jYiIiIiIDKe7c2NE6AhhbGtrgzl3T8WcWXehubkZJ04lYvPOG4vBtleXAYBr16vxzcbt+Gbjdtja2mDGlAlYOHsm5s6ahkPHY4Vzp4yPbrdm1NXUajUWzJmFqRPHIf5MChKTU1Hf0KB3XmNjE+Liz+BUQhIGDeiP8dGRCOzrL0PGZA78fDzx66cfxv6jsdi1/6iwiyMA1NbV48DhE/jhyEkMHRSCIQNDEBjgh77+vrCzswWgv9irQqFASP9gAIBWq0VZeSWu5uXjam4+EpPP4dr16nbz6evnjVWL5iDA18u0T5SIiIiIiIjIKAr0pN6CzuNrQGTJemZzIxFZDN1VSsNDRwrjAH9fPPPYKjzz2CpUVl3Djr0HsXnnPuz54SgaGhrbve/FKxl47Y0P8NobHyDAzwcLZt+NhXPuxqRxY3HomFjICwzwR7+gvqZ7UgZQqVRYMGcWxkdH4nRCEpLPnodG06x3Xk1NLQ4fO4ljJ09h+NDBiI4cAz9fHxkyJnOgVqkwf+YkjB42EGu/343CErFZV6vVIulsGpLOpsHbywNR4aHoFxiAvgG+sLezaz3vSoZYzLu1uVGSJFRWXcPVvAJcycjGqcRk1NfrF5lvCh85GEvn3gVHB3sTPUsiIiIiIiIiIiIiIiLLV1JartegGBk2sp2zb9QPJ4+PwuTxUXjj1d/jTMp5bN65D1t27kfapfR2r2toaMT2PT9g+54foFQqoVKJfw4ydWK0cU/EBJydnTB96kRMnhCNs+cvIi7+DErKyvTOkyQJl65k4NKVDCxeOAfDhw6WIVsyB1ZWSsyaOh6jhg7C2u+24arOLo7AjfdL2qV04fPDy8MdgQF+uHDponCup4cHtu89iLyCIuTmF972bw5uzcHg3RqJiIiIiIiIiIiow9jcSESykSRJb+fGyLBR7Z7v5uqC1cvuw+pl96G2tg57Dx7D5p37sGPvQVRdu97udXkFRXj34y/w7sdfwM3VBQ72YvPVlAlRsq1SepOnhztmz5qOaZPHIyn1HE4nJLf5nFpaWpB6Lg2p59Iw867JGBcVKUO2ZC6CA3zx26dXY+fBk9h/TNzF8abikjJs232gdezp7oa+AX7o6++D0wlJwrnWNjbYuvsAcvMKkJNXgJraujvm4Ohgj2Xz7sKYESwqExERERERERERERER6UrQWezV3t4OQwb2b+dskUKhQHjoCISHjsDffv9LXE7Pam10PJWY0u51Wq0WTU1NQmzK+CjDk+8iarUaY0JHImz0CGRfzcWpxGRcupLRZq3rRFw8mxvpx10cH0Jq2mUcPXUGF69k3fb8krJylJSVIzExUYhb29jg0LG4Ds3p6GCHcRGhmBgVBndX507nTkRERERERERERLfH5kYikk1G1lW9Br6IsBEdutbBwR73zb0b9829GxqNBoePn7pRyNu1H4VFJe1eV1FZhYrKKiE2dWKMwbl3FTs7O4yLikR0ZDgup2ciLv4Msq/mtnnuwSMnEBE2GtbW1t2cJZkTtVqFBXdPwtjQYTgcm4jTyRfQpNG0e35peQVKyytwJuUcLl4WV/fNyS/C3h+OdmheNxcnTBg7GhMjR8PB3tao50BERERERERERERERNRT6S72OmbUML1dFTtq0IB+eOkXT+ClXzyB/IIibN39A7bs3IfDJ06jubm53eu8vTwwZFBIp+bsSgqFAv2CA9GvXxAqK6twOjEZSannhN30CgqLUVtbBwcH+9vciXoDpVKJ0BFDEDpiCIpLy3Hs1BnEJqSivqGh3WuqqqqEsbPznZsU+wX6Y3JMOMJGDoH65udqG423REREREREREREZBpsbiQi2cQniYU8by8PBPj5GnwftVqNGVMnYMbUCXj3n6/gdGIKNu/ch8079+FKRvYdr58yIdrgObuaUqnEkEEDMGTQQBQVl+B0YhJSz10QipLNzc3IvpqLQQPNrxBJ3c/P2wMrFtyNhbOmIC7pPI6eSkJxaUW757e0tKC6ulqIubi43HGeYQP7YVJUKEYM7g+lUvljlMU8IiIiIiIiIiIiIiKitiQkiTs3ho/u2GKvd+Lv54OnH12Jpx9dicqqa9ix9xC27NqPPT8cRX292Ox135y7oVAoTDJvV3F1dcHd06dgysQY/OedD9HU9NNinumZWRg9criM2ZG58fZ0x6I5MzBv5hQknbuIjOxcXM0vREFRCVpatAAASZJQWloqXNdWc6NTHwcE+vsi0N8Ho4cPRl8/7255DkRERERERERERHQDmxuJSDa6q5RGhI40uqimVCoRHRmG6Mgw/ONPv0HaxSs/Njrux5mUc3rnDxsyEMGBfY2as6v5eHth3r13Y/qUifhq/SYUFBa1HruSkcXmRhLY2dpgaswYTIkOw+XMHJw5dwlX84tRUFSK5paW1vMqKvQbH9sq5tnZ2iDQ3xv9+/ojesxweLq7dmn+REREREREREREREREPYUkSUhIFmuUkWEjTT6Pq4szVi1dgNXL7kNdXT32HTqGzTv3ISk1DcOHDMRf/u95k8/ZVWxsbNA/OAgXL6e3xtIzs9ncaGKSJKGy6jpy8guRk1+IvMIS1NXVQ9PcjGZNMxRKJazVKqjVKri5OCMwwA+B/j7o6+cDa2u13Om3srZWI2rMSESNufF5pWluRmFRKXLyC7Hn4BFUVlYK50dHjsHI4UPh4eaCQH8fBPr7wtnJ0eybf4mIiIiIiIjapVDcePR2fA2ILBqbG4lINgk6OzdGmLiQp1AoMHzoIAwfOgh/+PUvcDUnD1t27cPmHXtxPC4BPt6eeP/ffzXpnF3J3t4ew4YMFJob0zOzIUkSiy1doKVFi+LScuTkF6GsogpNPxbyWrRaqFU3Cnn2drbw9/FCoL8PHB3s5E5ZoFAoMDgkCINDggAAzc0tKCguQ05BEXLyi/HeR2uE852dnTB0YD/Y2ljDz9sTgf7eCPL3gburM99fREREREREREREREREnVBQVIKiEnHnuIhQ0+zc2B57ezssmD0TC2bP7NJ5utKA/sFCc2NG1lVotVoolUoZs7J8VderEZeYivTMHOTkF6Kmtq7D1546c6O2r1Ao4OPlgaAAX4wePhgjhgyElZX5fFzUKhUCA3wRGOCLf77xjnBsQP9g/OuVX/N9REREREREREREZGbY3EhEsmhpacGZ1PNCLCLU9KuU3iooMAC/ePIR/OLJRyBJEiRJsrjCxYD+/XDg0LHWcWVVFSoqKuHu7iZjVj1DdU0tzl/KwNW8AuTkFyOvsBgaTXOHr3dzcfpxdU9vDAjui5Agf7NqClSprBDo741Af2+Uh1TisWeeF44/9fAKvPizFfIkR0RERERERERERERE1APF6yz26uzUBwP6B8mUjeUY0D9YGNfV1aOgsAgB/n7yJGTBJEnC5cyrOBqbiJTzl6DVao2+X2FxKQqLSxGXmApXZydMiB6D8ZGhcOrjaKKsjXclIxM79hwQYr946lGL+/sAIiIiIiIiIiKi3oDNjUQki4tXMlCrsxJkVzc33kqhUJhV41lHeXt5ok8fR1RX17TGrmRksbmxkyRJQmZOPo7GJiLp7EU0t7R0+l4VVddRUXUdyecvAwC8PdwwMSoU0WNGwN7O1lQpm8RHX3yL+vqG1rFKpcLPH1stY0ZEREREREREREREREQ9T0Ky2NwYPno4m6s6wMXFGZ4e7igtK2+NpWdms7nRAE1NGpyIT8axuEQUlZR12TyV165j+97D2HXgKMJGDMGU8WPRPyigy+brqLc++BSSJLWOXZyd8dCKxTJmRERERERERERERO1hcyMRySIh6Zww7uvvC28vD5mysRwKhQID+gcjKeWn1y89MxvRY8NlzMryNDY1IT75PI7GnkFeYXGXzFFcVoGNOw9i275jiBw9FJOiw9DXz7tL5jJEU1MT3v1krRBbsuBe+Pv5yJQRERERERERERERERFRz6RbEw0PHSFTJpZnQP9gvebGKRPHyZiR5bickY21G3egvKLqjufa2dki0N8XfQP84OHmBmu1Ciq1CpJWQpNGg4aGBuQXFuNqbgGKSkqFhsFbtbRokZCShoSUNMREjMaiOTNgJ9MCsJWVVVjz9QYh9viDy+Ho6CBLPkRERERERERERHR7bG4kIlnEJ6UK48iwUTJlYnkG9O8nNDdm5+RCo9FArVbLmJVlkCQJCSlp+G77PtTU1nfoGjcXZ/j7+cDOzhbWajUUCgWaW1qg0WhQXlGJvPwiaJqb272+SaPBiYRUnEhIRejwQVg6bzqc+zia6ikZ7Lutu1Cg09D5wlOPyJQNERERERERERERERFRzyRJEhKSxebGyLCRMmVjeXy8PYVxfkER6urqYW9vJ1NG5q+hsQlbdx/EkdiEds+xVqsRETYSQwcNQGCAHzzcXaFQKDp0/8bGJuQVFuFqbj4SzqQiOze/zfNiE1Jw4UomVt4/B8MHh3TquRjj4y++QV3dT7VgKysrPPsE66FERERERERERETmis2NRCSLxOSzwpirlHZc/+BAKBSK1lUxm5ubkX01FwMH9Jc5M/N2rboG6zbvQUra5XbPUalUGDZ4AIID/REY4Ie+/n7oc4cVPFtaWlBUUoac3Hzk5BXgYnomikvK2jw3+fxlXMnMwZJ50xExamiHC4WmIkkS3vjgMyE2IToCEWwuJiIiIiIiIiIiIup6kvbGw1wolMZcbLI0DNbOrmEd0s2/lzcdI54zAKM+Xsa83oAFv+bGy7qah4rKKiEWcceaqLGvl2W/3pIkITPrKuLiz+BKRpbesdz8fAweOECm7Npi7Oem6dxpt0ZvLw9MihmLqIjRsLfrXIOojY01QoIDERIciGkTY3A1Nx9HY+ORcCZVbzHYqmvVeO+zb/V3cTT2a8odaDQavPPRGiG2aP5s9A3wg3EfLxk/1ka9ZubzHjWMpeZNBuvirwlE1MvwawoRkXn9HsaccukV+HoTkWUzo+ZGc/qHhYV+cZfzH2dG/QAi58eehTw5NDU1IfncBSHWsZ0be+9rBtwoxKSeu4BTCWdaGxtvyrqaw+bGVuL75OZujRu27UVtXdu7Nbq7uWJSTCRixobB0eH2zYy6rKys4O/rDX9fb8SMHQNJknAlIxtHT55G8rkL0GrFP1SprW/AmvU7cObsJSxbcPctuzh2/dfC47GnkaizQvALTz9m/Ncjmf6ORPfzgDrC2K+jfM2pGxj3hcF0eVAH9cLXnO8zoi7Ezy/qIGMaAoz9Om5OzQhERERERGTWEnQWe/Vwd0VQX3+ZsjFvTU1NP9ZBk1BaVt7uebY2tt2YlWWQJAm7fziGHfuPtnk8pF8g5tw9DYNC+pl84dWgvv5Y1dcf982ZiZOnzmD3gSNoaGwUzolNSMGl9Cw888hy+OrsxtkVvt+6C3n5hULslz9/vMvnJSIiIiIiIiIios4zo+ZGIuotzl+8gsbGJiHGnRvbd+3adcSfSUZicirq6xvaPEer5R8Bt6WxqQlfbtiOpHMX2zw+bPAATJ0QjaGDB0CpNGaF6p8oFAoMGtAPgwb0Q9W16zh5KhGHT5xCTW2dcF5K2hWkZ+Vi9ZI5GDmke1aYfeODT4Vxv6C+mH/vjG6Zm4iIiIiIiIiIiIiIqDeJTxKbGyNCR5i8uczSVVVdQ/yZFCSmnEVDQ2O75ykUCsSMDUcgm0MFWq2Ejdv34fDJeL1jarUaC+6dgcnjx5qsDtoeB3t7zJg6AeGhI/DVd1tx8XKGcLyi6jr++78v8fNHlyPI37fL8pAkCf997yMhNi4qAmPDw7psTiIiIiIiIiIiIjIemxuJqNvpFvIG9A+Cq4uzTNmYJ0mSkJdfgLj4JFy4dEVv979bOTs5YWx4aPclZyHq6urx3ufrkZWTr3fM0cEBy+6bgzGjh3dpDi7OTrh35lRMHDcW6zfvxJkUcdfE2voGfLj2e6xeNAdjQ4d1aS6Z2TnYsnOfEHvuiYdgZWUF7pBDRERERERERERERERkWonJYl0oInSkTJmYn5LSMhw+FosLl69AkiQAbTd92tnZYszokRgbHgpnZ6fuTdLMabUSvv5+B2ITUvSODegXhAeWLoCXh3u35uTm6oJnH1+NE6cSsWn7XmEXx9q6erz54Vf4+aPLERIU0CXznzyVgPgz4uvxwtOPdclcREREREREREREZDpsbiSibpeQrLtKKQt5N2m1WpxLu4i4+DMoKCy+7bke7m6IihiD0SOHwdraupsytAzVNbV4+5NvkF9UondszOgRWLpwNvo4OnRbPn0cHfDYqiU4M2o41m3agZra2tZjWq2EL77bjobGRkyK6rpVQ9/+cM2PxeEfc+rjiEdWLumy+YiIiIiIiIiIiIiIiHorrVaLxJTzQiwidIRM2ZiX0rJyfLZ2ndD4psvTwx1REWEYNWIo66BtkCQJ3+/Yr9fYqFAosHD2TEybFNPluzW2R6FQYEJ0BIYNHoBPv9qArKt5rccam5rw/pr1eOGJBxDg623yud9472NhHBQYgAVzZpl8HiIiIiIiIiIiIjItNjcSUbdLSEoVxhFhbG4EbhShNm7ZgbSLV2573oD+/RAdOQYh/YOhULS9imlvVt/QgHfXrNNrbLS3s8OKRfO6fLfG2xkzejgGhgRj3ffbkXQ2rTUuScC6rftgrVYjeozpC9vXrl3Hp19tEGKPrVoKJ6c+Jp+LiIiIiIiIiIiIiIiot7uckYXqmlohxubGG7Ku5rTb2DhoQH9ERYShf78g1kFvY8/B4zh04rQQUyqVeGj5/WZTe3dzdcGzP3sQH33+LS5eyQQAlJaWIjExEZ98+gkc7O1aP8aSdKNWfvNxI9beGJDQ9jk1Op9zzz3xCFQq/lkUERERERERERGRueNv8YioW9XXN+DcBbF5jzs33lBVda3dxka1Wo3QkcMRFRkGD3f3bs7McjRpNPjg8w3IzS8S4s5OffDszx6En4+XTJn9pI+jAx5dtQSbduzFwaOxrfH8/Hw8+uxv4O3hCidH+9biHNB+8Q64Uby707nlFZVCMU+pVOK5Jx7q+idLRERERERERERERETUC8UnnRXGfj5e8OuCneoskcpK/DMVhUKByDGhiIoIg7u7m0xZWY6M7Fzs2H9EiCmVSvzswWUYNXyITFm1zdbGBk89+gDe+/hLfPn1OiQnJ7fWMK9fr+7SuR0dHfDoqmVdOgcRERERERGRWVAobjx6O74GRBaNzY1E1K1Szl1Ac3Nz61ihUGDMKPl20jMnmltel5tcnJ0xNiIUY0aPhK2trQxZWZZNOw8gPTtXiLm5uuD5Jx+ChxkVQ5VKJe6fOwu2NjbYtH0PTp48iUuXLgEA0u5wrSncN3cWggP7dsNMREREREREREREREREvU9C8jlhHGkmu+mZAx9vT73Y9KkTYW1tLUM2lqWpSYO1321vXfQUuFFvf3D5fWbX2HhTQmIS/vfRx7h0Ob1b53101TI4Ozt165xERERERERERETUOWxuJKJulZAsrlI6dFAIHB0dZMrGvLi7ucLKSomWFm1rbP7su9EvOFDGrCzHpfQsHI1NFGJOfRzx3BMPmlVj400KhQKN9bXYvGkTqq5d69a5X3jq0W6dj4iIiIiIiIiIiIiIqDdJSBKbG8NDR8iUifnx9HCHQqFo3cFPkiSUlJYhwN9P5szM37a9h1FSViHE5t1zFyLDRsmUUftqa2vx+1f+jrc/+Lj1Y91dRg4fgj+99EK3zklERERERERERESdx+ZGIupWCUlic6M5FlrkYmVlBS9PDxQWlbTGiktK2dzYAQ2NTfhq4w4hplar8fPHV8PLw12mrNpXXFyCZ3/1f/hu07Zun3v5/fMQM3ZMt89LRERERERERERERETUGzQ3NyPpbJoQi2BzYyu1Wg13N1eUlf/UpFdUXMrmxjvIyM7FoROnhFi/oADMmDJBpozad/DwMTz2zAvIyr6qd0ypVCIsLAw+Pj4AAD9vT9w/ZzqslFZQKBQAbiwSq1Dc/O+tMUU7MbTGHB0cMGzIoNYxERERERERERERmT82NxJRt9LduZGrlIq8vTzF5sbSUhmzsRxbdv+A8kpx98MF985AgJ+PTBm1TZIkfL1uI37xm9+joqJS77harcaIESOgVqthZWWFqePC4WBnJxTvbl/Aa+c83BgP6B+EaZPGsZhHRERERERERERERETURdIupaOhoVGIsblR5OPlKTQ3FpewJno7Gk0z1n63HbdugKhSqbBq6UIolUr5EtNx7dp1/Pr3r+DjNWvbPB4eNhpLlyxCVk6BEFfb2GPahLHdkSIRERERERERERGZITY3ElG3qampxYXLGUKMOzeKvD09hHFxSZlMmViOSxnZOBqbKMQG9A/C5PHmVQDLyy/Ak8/9Cjv37G/z+OSJ4xEY3B+Ojo6tMQ9vP/zi0eVQKtmMSEREREREREREREREZAnik8TFXoMD/eHh7iZTNubJ28sT5y5cah0Xl7ImejvxyedQUlYhxObOmgYfL0+ZMtK3c88+PPHsr5BfUKh3zMbGBn/5w0v45XNPoaVFi1f/+x7Kyn9aCHbPoROYGD0GahX/hImIiIiIiIiIiKg3Mp8l3IioxzuTeh7SLctJqlQqjB4xVMaMzI+Pt5cwLiktg1arlSkb8ydJEjbtOCDE1Go1Vi0xn1VKJUnCx2vWYnjEhDYbG93cXPHVpx/g0O7NmDV9inDsSlYuzl5M76ZMiYiIiIiIiIiIiIg6SjLyIRNJMu5B1AEJyeeF8Y3FXhUdfBhJoej8w2gd/xrg4y025RUXl0LSavl52QZJknAkNkGIBff1x12TxsmUkai8vAIPPPIU5ty/ss3GxgnjopB66gh+88tnoVKpYGNjjQeWLBDOqamtQ/K5S3rX6jPye49sX/8t9HsmWRb+jEM9naw/4xAREVmSjv7+oQt+J0FERGQE8+j8IKJeISFZXKV05LBBsLW1kSkb8+Sts7pmc3Mzyisq2zmbsnLykVtQJMQW3DsDnh7msfptZlY2ps++Hz/7+S9x/Xq13vHF981DWsJxrFy2CAqFAgvnzISbi7NwztFTZ7orXSIiIiIiIiIiIiIiIjJSgs7OjeGjR8iUifny9vQQxo1NjaiquiZTNubtal4BcvPFeug9M6bIvtCrJEn4btNWDAufgK/Xb9Q77uBgj3f+8xqO7N2GQQNDhGODQvphYEiwEDsal9iV6RIREREREREREZEZU8mdAAAzXAVKplwseaUguT5+Rr9mcq4Ma8S1Rj1t+Z6zXiEvdGT3TW4hn1/29nZwcnLE9es1rbGi4hJ4erjLmFVndM/77KjOKqXubq6YPH5st8x9Oy0tLXj3f5/gd6/8HXV1dXrHvb088f6b/8R98+cIcTtbW8y+exrWrt/cGrtwJRsl5VXwumPDphGvubEfLmM+vWT9/m9E4nJ++5H19TZqciOnNmJuS33exn7vMqufr4nMDD8/qMfje5y6gaSVOwN5GPM9pK1r+T2JiIiIiKhHaWxsQmqauANdZFg31kQtRJ8+jrCztUV9Q0NrrKikFK6uLvIlZaaOxopNf26uLhg+ZKBM2dxQVFSMp194CZu37Wzz+PSpk/Hxe/9FcFBgu/eYPG4srmRkt44zsnORV1iMAF9vU6dLREREREREREREZo47NxJRt9HduZGFvLZ5e4q7NxaXlMqUiXmrrqnFmdQLQmxiTITsq5ReuHgZE2fMxfO/+UObjY0PrlyKtMQTeo2NN4WHjoC9nZ0QO3YqqUtyJSIiIiIiIiIiIiIiItNJTbsEjUYjxMaMHi5TNuZLoVDAx5s10Tupqa1DQsp5ISZnPVSSJHzx1ToMi5jQZmOjs7MTPn3/Tezb/t1tGxsBYPSIoXDq4yjEuHsjERERERERERFR78TmRiLqFpVV15CeeVWIRXTnzo0WxNvLQxgXl5TJlIl5i01IQXNLS+tYpVJh3NgxsuWj0Wjw2r/eRGjMVMSeitc73jfAH7s3r8PnH70LNzfXdu9jrVYjZmyYEItNTEVTk6adK4iIiIiIiIiIiIiIiMgcJCSfE8aDB/SDs1MfmbIxb6yJ3ll88nk0N99SD7WywrhIeeqhObl5uHfhMjz0xLOorKzSOz5/zj1ISzyBRx5cCYVCccf7WVlZYUJ0hBCLTzoHjabZVCkTERERERERERGRhWBzIxF1i8QUsZBnY2ONEUMHyZSNefPx9hLGRSUlMmViviRJwrFTZ4TYmNHD4ejgIEs+ySlnETX5bvzulVfR1NSkd/ypxx/GufhjmDXzrg7db2JMpDCuq2/AmbMX2jmbiIiIiIiIiIiIiIiIzEF8klgT5WKv7fPxYk30TjKyc4Vx6Mhh6KOz22FX02q1+ODjNRgeMQF79h/UO+7h4Y51X3yEzeu+gJ+vj0H3Hh8VLjRCNjZpkF9UbHTORERERERERL2KQsHHzQcRWSw2NxJRt4hPOiuMQ0cMhVqtlikb8+bt5SmMq6trUFdXJ1M25qm8ogrlFVVCbPK4sd2eR2NjI17+y2uInDQTSSln9Y4PCOmHw3u24P03/wknA1bl9fJwx7DBA4TYxfRsY9MlIiIiIiIiIiIiIiKiLqS74GtEGJsb26O7c2Nl1TU0NjbKlI15ys0vFMaDBvTr9hx++dLLePr536Cmplbv2PLF9yEt4TiWLlrYod0adbm6OMPLw12I5eQXdTpXIiIiIiIiIiIiskxsbiSibpGYrFvIGyVTJubP3c0VKpWVECsqLpUpG/N0NU8s5Dk6OCA4MKBbc7h+vRrRU2bhb6//F83NzcIxpVKJF597GilxhzF54vhO3X/ksCHCOKeAhTwiIiIiIiIiIiIiIiJzVVtbh/MX04VYROgImbIxf54e7lAqxT9ZKS0rlykb81NX34DS8kohFhjg1605pJ49j7fe/0gv7uvjja0b1uKbzz+Ep6dHG1d2XGBf8Tnp1oGJiIiIiIiIiIio52NzIxF1C92dGyNCuUppe5RKJbx0ikDFJWxuvFWOziqlgQG+nVoN1Bi/+cOfkZx6Ti8+fOgQxB7ajX+/9mfY29t3+v6BAb7CuLi0HA2NTZ2+HxEREREREREREREREXWd5HMXodVqW8dKpRKhI4bKmJF5q69vEF4vAHrNjr2Z7q6NKisr+Pl4dWsOn335jV7s0QdXIi3xBObNnmWSOXQbNnWfNxEREREREREREfV8/M0wEXW54pIyvSJEZBibG2/HykrcuVGjszNgb6ff3Ni9q5TW1NTg6/UbhZhKpcIf/+9XSDxxAGMjxhg9h7+fj1DAlSQgr7DY6PsSERERERERERERERGR6eku9jp8yEA4OHR+IcyeLjM7RxjbWNvA28tTpmzMT05+kTD28/WGSqXqtvkbGxvxlU499KVfPotP3n8TLi7OJpsn0F+s8xYUl0GjYW2ciIiIiIiIiIioN2FzIxF1uYRksZDn4GCPwQP7y5SN+WtoaEB+gU6xysdbpmzMjyRJyNUp5nV3c+PGzdtRU1PbOlYqlYg7vAd//sNLsLGxMckc1mo1fL3FAq5uEZOIiIiIiIiIiIiIiIjMQ2LKeWEcHjpCpkwsQ3pmtjDuF9RXbwHY3ixfZ9HT7q6Hbt+1F+XlFULs8YdXmXyevv6+UCgUrWOtVovCklKTz0NERERERERERETmi82NRNTlEpLPCePw0cNZmLqNrKu50Gq1rWOVSoWgwAAZMzIv5ZVVqKtvEGLdXcz7bO03wviemXchPGy0yecJ7OsvjNncSEREREREREREREREZJ50d26MDB0pUybmT5IkZGRdFWID+gfLk4yZqmtoFMZuri7dOv9nX4r10MkTxyGkfz+Tz2NrawN7Ozsh1tDQZPJ5iIiIiIiIiHouBR+tDyKyVCq5EyCini9Bp5AXzkLebaVnZgnjoL4BUKvVMmVjfqqra4WxWq2Gq4tzt81/+UoGjp2IE2KPrF7RJXN5e7oL41t3iyQiIiIiIiIiIiIiIiL5SZKE3/71P7ickS3EI7hzY7uKiktQV18vxEL6BcmUjXnSaDTC2FrdfX/ek5dfgL0HDgmxR1Z1TT0UANQ6z03TrGnnTCIiIiIiIiIiIuqJ2NxIRF3q6Ml4HDpxSohFho2SKRvzJ0kSMjLFVUpD+rOQdytNc7MwtlaroVB032obn3/1rTD28HDHnHtmdslc1mprYaxpbumSeYiIiIiIiIiIiIiIupdkxLUyrsAtGZF3N9Yy9Fno623Ma2b0692x61taWvDkr17Bp199J8Q93F0xasQQGT7uxszXfbmmZ2YLYzdXF7h2886E5q5Zpy6oUnXfn/d8+c16aLXa1nGfPo64f8GcLptPr7lR09zOmTIy5us/dT/ZPl6W/D6x5NwtkTHfc/mxIiIiotvhzwpERGSZ2NxoToz95ZqsBTGZyPkLZIt9vY3M24DnvWPvQSx55Dk0NDQK8ajw0V0+dxsXy3StYSoqKlF17boQu7FKaWff65b6Pm2fbjFLt9jVlVpaWvDF1+uF2ANLF8Ha2rqdK4yjX8jriauUdv49auyXYYlFSCIiIjJb/DmFOkjS3vkcIiIiIiIi6hKNjU1Y9dSvsXH7Xr1j//7L/8HGxkaGrCxDRpa42OuA/sHyJGLGlEqlMG7Rds/vACRJwmdffiPEli1aCAcHhy6bs6VFfG5WVlZdNhcRERERERERERGZH+WdTyEiMtzX323FwlVP6zU2rlqyAP2C+sqUlfnTXaW0Tx8HeHl6yJOMuZKxsXjfgUMoKCwSYo+sXtF9CVhsUzUREREREREREREREVHPUVNTi3kPPKnX2GhlZYU1776O1csWypSZ+WtqakJufoEQC+kXLE8yZkyts1NjczftZnjsRCwydGrWXV0PbW7WWdy2G3epJCIiIiIiIiIiIvnxN4JEZHLvfrwWz/32L3rxBffOwIdvvCpDRpZDd5XSkH7BULChTaC/m2H3FPIA4LO14iqlEWNCMXLEsC6bT85dKomIiIiIiIiIiIiIiEhfRWUVZi9/AqcSU4S4tbUa6z5+Awvm3C1TZpYh62qusFOfUqlEcGCAjBmZJ2trtTCuq6/vlnl1d20cOmQQoiLDu2w+rVaL+voGIcaaKBEREREREZEBFApungLwNSCycNy5kYhMRpIk/OVf77TZ2PjQ8vuwYc07sLW1kSEzy9DS0oLsnFwhFtIvSKZszJfuSp1NTU2QJKnL5y0rK8fWHXuEWFevUtrU1CSMuUopERERERERERERERGRfAqKijF53iq9xkZHB3vsWvcxFtw7XabMLIfuYq+BAX6wsWENWZeHu6swziso6vI5q6tr8N3m7ULs4QeWd+livMWl5dDo7Nyo+9yJiIiIiIiIiIioZ2NzIxGZhFarxfO/+xte+cfbeseef+phfPL2a1CxMeu2cnLz0dSkaR0rFAr0D2Zzoy5nJ0dhrGluRnlFZZfP+/X6jdBofvr42NraYvni+7p0zoLiEmGs+9yJiIiIiIiIiIiIiIioe2Rk5WDC7BU4f/GKEHd3c8EPm7/AtInRMmVmWXSbG7nYa9sC/X2EcU5eQZcv+Lph0xbU1dW1jq2srLBq+eIunTMnr0AYOzs5wrkPa6JERERERERERES9CZsbichoGo0GDz/zEt756Eu9Y3/93Qv4z1//D0olv9zcSUZWtjD29fGCg4O9PMmYMVdnJzjqvC66RS9TkyQJn37xjRC7b95suLg4d+m8uTrPK9DPp50ziYiIiIiIiIiIiIiIqKuknr+EiXNWIjsnX4j7+3rj6PavERk2UqbMLEtlZZXeoqVsbmxboL+vMK6uqUXVtetdOudnX4r10NmzZsDHx7tL59Srh+o8byIiIiIiIiIiIur52G1EREapr2/Aood+jrUbtghxhUKB9/71Cn7/4tNQKBTyJGdhuEppxygUCr2i1tXcrm1uPJOcirPn04TYI6tXdOmcDY2NKCopE2K6K7QSERERERERERERERFR1zp5+gymzF+FopJSIT6wfxCO7/wGQweFyJSZ5dGth9rb2cG3i5vnLJWnuyvsbG2EWFcu+Hrx0hWcjIsXYl1dDwX0nxProURERERERERERL0PmxuJqNOuX6/GvUsfxfY9B4W4SqXCVx/+B089slKmzCxPTU0tCotKhFhIv2B5krEAgQFiUSsnv2ubGz/78mthHBTYF1MnT+jSOfMKiiBJUutYqVTA39erS+ckIiIiIiIiIiIiIiKin+z54RhmLHpEb8e80BFDcWzHNwjq6y9TZpaprcVeuVBu2xQKBfrqNPpl5+R12Xxr1n4rjL08PXHv3dO7bD4AaGlpQW5BoRALDODOjURERERERERERL0NmxuJqFNKy8oxbcEqHDlxWojb2dliy1cfYPn9c2XKzDJlZouFPGtrNfoG+MmUjfnT3bkxJ69AaAQ0pfr6enyzYZMQe3jVciiVXfstVHeVUl8vT1ir1V06JxEREREREREREREREd2wfvMuzF/1NOrrG4T4xOgIHNr6Jbw83WXKzDJptVpkZucIsZB+QTJlYxkCderF8UlnodVqTT6PRqPBF1+vE2KrVyyBuotrkynnLqCxsUmI6daBiYiIiIiIiOhOFHy0PojIUrG5kYgMlpNXgEmzV+BMynkh7uzUB3s3rsG9M6bIk5gF012ltF9QIKysrGTKxvzpFrXq6xtwOSOrS+basn03qqqutY4VCgUeemBZl8x1q+SzacI4UGdlViIiIiIiIiIiIiIiIuoaH36xDiueeBEajUaIz5k5BXs2fAJnpz4yZWa58vIL0dgkNrKxufH2xowcIowrKqtw/uIVk8+zZ/9BFJeUCrGHVy03+Ty6jpwUF1LuHxQA5z6OXT4vERERERERERERmRc2NxKRQS5dycTEe5fhUnqmEPfydMfh7V9jQnSETJlZLkmS9JobWci7PVcXJ/h4eQixoyfju2SuNWu/FcZ3TZmEoMC+XTLXTQWFxUjPFN8Twwb179I5iYiIiIiIiIiIiIiIejtJkvDaWx/jqV+9AkmShGMrF83F95+/Azs7W5mys2y69VBvTw/0YSPbbQUF+Okt+npUpyHQFD778hthHBUZjmFDB5t8nlsVFpXgSka2EJscE96lcxIREREREREREZF5YnMjEXXYmZTzmDh7GXLzC4V4UF9/HNu5DqNHDJUpM8tWXFKKmppaITagf7A8yVgIhUKBidFicSvl3AVUXbtu0nmu5uTiwKEjQuyR1StMOkdbdFcpdXJ0wOhhg7p8XiIiIiIiIiIiIiIi8ycZ8ZCRJHX+QZ2gMPghScBv/vwf/P7VN/Xu9vPHHsAX770OtVrdtVkrlEY9YNRDYdzjDq+v/mKvwT8dN2renkuhUGCSTsNf2qV0lJZVmGyO2tpa7Ni9T4iNHD5Ur7nX1I7GigvXOjrYI3TEkHbOJuol+LMCEZFx+DMlEREREZHFYnNjm3phQczYR29k9GtmWe+zIydOY+qC1SgrrxTiwwYPwPFd32Jg/6BueJ8YXoQ0SUHM6ELe7ekW8lxdnOHq6mL8c0bP/sVL9JiRsL6lgKzVanE8LsGkc6z9doNQuLOxscG82XebdA5d9Q0NOH0mRYiNjxwNlcrqDlfyfUId0MVfz+4wuZEPY6aW83kTEVHvZVn/5iMiIiIiIiLqzZqbm/HYCy/jP++v0Tv2x18/g7f+/nsolfzzis6qr69HfmGREAvpFyRTNpYlYvRwYbdQSZJw+EScye5fXV2D5uZmIfbJ519h6erHcM3EC8veVFdXj1MJyUJs/NhQqFWqLpmPiIiIiIiIiIiIzBt/+05Ed7Rtz0HMWvoYqnV2Fxw7ZhSObP8a/r4+MmXWM2RkZgvjkH5BULCZ5o7s7GwxNmyEEDtxKlGv+GaM9IwsYdzY2IgFSx9EcXGJyebQdToxBY2NTa1jhUKB8WPDumw+IiIiIiIiIiIiIiKi3qyxsQnLHn8Ra77ZpHfsjb/9Dq/85lnW7oyUmZ0jLCiqVqkRFBggY0aWw9pajZjw0ULsyInTyMkrMMn9fXy8sXzxfXrx7zZtw5jxdyHhTLJJ5hHuvXU3GhobW8cKhQITxo4x+TxERERERERERERkGdjcSES3tXbDVtz/0LNCsxUA3DUpBvu//xzubq4yZdYzNDU14WpunhAbENJPpmwsz8SYcGF87Xo19h08ZrL7T5s8US924NARjI6egh8OHTXZPDdV19Ri577DQmzU0IFwc3Ey+VxERERERERERERERES9XXVNLeaseBKbdu4X4lZWVvj83X/gF0+slimzniUj66owDgr0h4q79HXY5HHhws6hWq0WX67bZLJFXz//6B288PMn9eKZWdkYN+1evPXeh0JzqjHOpl3CqcRkIRY6YjDcXZ1Ncn8iIiIiIiKiXkeh4OPmg4gsFpsbiahdb334JR585iW0tLQI8fvmzMSObz9Gnz6OMmXWc1zNyUNLi7Z1rFQqEcxVSjusr58PQoL7CrFdB44gr6DIJPdftWIJXn3ld0KxEACKS0oxY+4i/Olvr+t9fhhjw5adqKkVd0idHMNVSomIiIiIiIiIiIiIiEytvKIKMxY9gh+OxQlxGxtrfP/5O1i9dIE8ifUwkiQhPTNbiIX0C5YlF0vl6e6Ge6ZNEGIFRSXYfeCISe5vbW2N/77+V2zdsBauri7CMY1Gg+d/8wcsXPYgKioqjZqnrq4e32zcJsRsbWywaM4Mo+5LRERERERERERElo3NjUSkR5Ik/On1t/HCH/6ud+zhlYuw7pM3YWNjLUNmPY/uKqUB/r6wtbWVKRvLtGjuDCiVP622odVqsXb9ZpM0HSoUCvzu1y/g8J4t8PfzFY5JkoS/vPZv3DX7PhQUGt9MmZR6HonJ54TYqKEDMTgk2Oh7ExERERERERERERER0U/yC4sxef4qnD5zVoj3cXTA7nUfYd6saTJl1vOUlVfgenW1EBvQP1ieZCzYrGnjEeDnLcT2HjyGnLwCk80xb/YsJMceQkxUpN6xrTt2I2zcNMSdTuj0/b/buhvXrovvhUVzpsPV2anT9yQiIiIiIiIiIiLLx+ZGIhJotVo8939/w1///b7esRefeQSfvPkqVCqVDJn1TPqrlAbJk4gFCwrww4zJ44RYbn4h9v5w1GRzTBwfg+TYQ7hn5l16x44cO4nR0VOwZ98Pnb5/dU0tvv1+hxCzt7PF8gWzoOA26URERERERERERERERCaTnnkVE+c+gLRLGULcw90VBzd/jinjx8qUWc+UkZUtjJ369IGHu5s8yVgwKysrrFo8F0rlT3/mo9Vq8enaDXoNg8YI7BuAI3u34qVfPqt3LCc3DxNnzMW/3ngXWq3WoPsei43HqcRkITZ8cAhiIkYbky4RERERERERERH1AGxuJKJWGo0GDz7zEt779Gu9Y6/+/gX880+/ZqOVCV27dh1l5RVCjKuUds690yfC19tTiO06cESvedQYHh7u2PH9N/jn3/6k1+BbVlaOexYuw//98a/QaDQG3belpQVr129GTW2tEF8ydwacnRyNzpuIiIiIiIiIiIiIiIhuSDl3ERPnPoDsnHwh3tffB0e3rUX46OEyZdZztbXYK2vOndPXzwf3TJsgxErLK/DOR1+gtq7OZPOo1Wr8469/xO7N6+Dh4S4ca25uxm/+8GfMW/wAysrKO3S/+KRUrNskLvRqa2ODFffdy/cCERERERERERERsbmRiG6or2/A/Q89i683bhfiCoUC7//rFfzf80+wsGBiGVlXhbGdnS18fbxlysayqVUqrF4yF0rlT+9RrVaL9z/7Gjl5BSabR6lU4tcv/BxH921DYN8AveP/+M/bmDJrPnLz8tu4Wp9Wq8VXG7bi3IXLQnzUsIGIDGXxnIiIiIiIiIiIiIiIyFQKi0sx7b6HUFyq35C1ZP49KCuvRHVNbRtXUmc1Nzfjqk4jKRd7Nc6saeMR6O8rxAqKSvD2h1+Y/P07a+ZdSI49hEkTYvSO7dyzH6ExU3HsROxt7xF/JhVffLsJkiQJ8SXzZsLV2cmk+RIRERERERH1Tgo+Wh9EZKnY3EhEaG5uxpwVT2DHvsN6x/780nP42eol3Z9UL5CemSWM+wcHQqnkl+XOCgrww4zJ44RYQ0Mj3v34S+TmF5p0rpioSCSdPIh5s2fpHTsZF4/QmKnYsXvfbe+h1WqxfvNOnEpMFuIOdrZYPn8Wm4mJiIiIiIiIiIiIiIhM6NtNO1FZdb3NY/95fw0mzVsFl5CxGBx9D5Y99gJef/tj7Dt0HKVlFd2cac+Rk5sPTbOmdaxQKNAvKFDGjCyflZUVnnp4KTzdXYV4bn4h/vv+p6ioumbS+fz9fPHDzk14+bcv6tUv8wsKMfWehfj7v96AVqvVu/boydP4/Nvv9Y7de9cERIePMmmeREREREREREREZLnYRUNE2Lh9Lw4dP9XmsT/+4y24hkRi8twH8IvfvYrPv92ElHMXoNFo2jyfOkar1SIzO0eIDejfT6Zseo45MyZj1NCBQqymtg5vfrBGb6dMY7m5uWLL+i/x5j//BrVaLRyrqKjE3EUr8eJv/4impia9a1taWvDFt5twLDZeiKtVKvxs1f1wdnI0aa5ERERERERERERERES9XUhw3zueI0kSrmRexYatu/F/f/0PZi15DN5Dx6HvqMmYt/JJ/On1t7Fl1wFczc3X24mO9OnW5/x8vGFvbydTNj2Hcx9HPPfYSrg49RHixSVleO2/7yP+TKpJ358qlQp/efm32LftO3h7eQrHWlpa8PtX/o5Z85eiuLgEAFBTW4vPvvoO6zbt0MtjyrhIzJ4+yWS5ERERERERERERkeVTyZ0AEclPo2m+7fHqmloci0vAsbiE1pi1tRojhw5G6MihCBs1DGEjh2HUsMFwcLDv6nR7hILCIjQ0NAqxkH5BMmXTc1hZKfHoivvw3pp1uJz5U7G0vqEBb3/0JRbMnoHJ48aabIdMhUKBXzzzBMZFj8XS1Y8jK1ss0P73nQ9wPPYU1n3xEfoF3/j4lpZVYO2GzUjPFM9VKpV4bOVCDOzH1WqJiIiIiIiIiIiIiIhMbd6safjXK7/Gh1+sR3pWzp0vuEV+YTHyC4uxY9/h1pibqzNCR9yslQ5F2MhhGBQSDCullYkzt1zpmdnCeED/YFny6Inc3Vzw/BOr8PYnX6Oi8qfdGmvr6rHmm404k3oey+6bA2edBkhjTJ82Gcmxh7Dykadw8Mgx4dj+g4cRGjMVf375/5BxNR/VNbV6108dH4n7Z8/Q2wGSiIiIiIiIiIiIejc2NxIRlsy/B99+vwN7Dh6788k/amrSIDHlHBJTzrXGlEolBg/oh7CRwxA6chjCRt0o4rm5unRB1pZNt5Dn6eEOJxMWlnoztVqFJx9cjA8+34ArtxSmNRoNvtuyC8mpaXhgyQJ4eriZbM7I8DAknTyIx555Hhs3bxeOnU44g7Bx0/DJe2/C3dMTW3fuR5POzqdWVko8smw+Rg4ZYLKciIiIiIiIiIiIiKg34M5xhjH29WJDjsGM2T3OxA1QCoUCLz79MF58+mFcu16NlPOXkHT2ApLPXsCZ1DSkXc5AS0tLh+9XUXkNB4/F4eCxuNaYvb0dRg0bjLBRw1sXiB0xdCBsbGw6mKSRC3Qae70JVVfXoLi0VIiF9Avugpnk/Lw05muKsXlL8PJww4tPPoi3P/kaxaXlwtG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      "text/plain": [
       "<Figure size 4800x2400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure S1---used in paper \n",
    "titles=['Wet season 2014-2019 mean','Dry season 2014-2019 mean']\n",
    "\n",
    "\n",
    "fig1, axes = plt.subplots(1,2, figsize=[16, 8], dpi=300,subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "axes = axes.flatten()\n",
    "\n",
    "(AOD_2014_2019_wet[\"AOD500nm_total\"]).plot(ax=axes[0],  cmap='Oranges',vmin=0, vmax=1,add_colorbar=False)\n",
    "     # Get all the data from the dataframe\n",
    "latwet, lonwet,sitewet = seas_ARNET_wet['Latitude'], seas_ARNET_wet['Longitude'],seas_ARNET_wet[\"Name\"]\n",
    "AOD_500wet = seas_ARNET_wet['AOD_500nm']\n",
    "axes[0].scatter(lonwet, latwet, transform=ccrs.PlateCarree(),\n",
    "        c=AOD_500wet, s=200, norm=norm,cmap='Oranges', edgecolor= 'dimgrey')\n",
    "\n",
    "im10=(AOD_2014_2019_dry[\"AOD500nm_total\"]).plot(ax=axes[1],  cmap='Oranges',vmin=0, vmax=1,add_colorbar=False)\n",
    "\n",
    "# Get all the data from the dataframe\n",
    "latdry, londry,sitedry = seas_ARNET_dry['Latitude'], seas_ARNET_dry['Longitude'],seas_ARNET_dry[\"Name\"]\n",
    "AOD_500dry = seas_ARNET_dry['AOD_500nm']\n",
    "axes[1].scatter(londry, latdry, transform=ccrs.PlateCarree(),\n",
    "                c=AOD_500dry, s=200, norm=norm,cmap='Oranges', edgecolor= 'dimgrey')\n",
    "\n",
    "\n",
    "fig1.colorbar(im10, ax=axes[1], label='AOD 500 nm', shrink=0.8)\n",
    "\n",
    "fig1.suptitle(\"Aerosol optical depths in South America\", fontsize=24)\n",
    "\n",
    "\n",
    "# Use for loop to save some coding. Useful when there are a lot of subplots.\n",
    "for i in range(0,2):\n",
    "    #plotting GEOS-chem results\n",
    "    axes[i].add_feature(cartopy.feature.LAND, color='0.98')\n",
    "    axes[i].add_feature(cartopy.feature.OCEAN, alpha=0.5)\n",
    "    axes[i].coastlines()\n",
    "    axes[i].add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=.5)\n",
    "    axes[i].set_extent([-85, -30, -56, 13])\n",
    "    axes[i].set_title(titles[i], size=20)\n",
    "    \n",
    "    \n",
    "plt.show()\n",
    "fig1.savefig('../PP2_figs/AmazonHealth_FigureS1a.png', dpi=fig.dpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AOD_2014_2019_wet.to_netcdf(\"GC_AOD500wet_2014_2019.nc4\")\n",
    "#AOD_2014_2019_dry.to_netcdf('GC_AOD500dry_2014_2019.nc4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>4023</td>\n",
       "      <td>2022-01-31 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.111798</td>\n",
       "      <td>0.148398</td>\n",
       "      <td>0.209221</td>\n",
       "      <td>0.235761</td>\n",
       "      <td>1.283426</td>\n",
       "      <td>1.310844</td>\n",
       "      <td>1.368162</td>\n",
       "      <td>1.284635</td>\n",
       "      <td>1.078179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4024</th>\n",
       "      <td>4024</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.019888</td>\n",
       "      <td>0.020804</td>\n",
       "      <td>0.025685</td>\n",
       "      <td>0.031816</td>\n",
       "      <td>0.812853</td>\n",
       "      <td>0.790364</td>\n",
       "      <td>1.203563</td>\n",
       "      <td>0.552298</td>\n",
       "      <td>-0.683922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4025</th>\n",
       "      <td>4025</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>0.080296</td>\n",
       "      <td>0.114358</td>\n",
       "      <td>0.183114</td>\n",
       "      <td>0.217191</td>\n",
       "      <td>1.445593</td>\n",
       "      <td>1.408980</td>\n",
       "      <td>1.510809</td>\n",
       "      <td>1.449293</td>\n",
       "      <td>1.140956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026</th>\n",
       "      <td>4026</td>\n",
       "      <td>2022-02-28 00:00:00+00:00</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>0.085947</td>\n",
       "      <td>0.124275</td>\n",
       "      <td>0.202389</td>\n",
       "      <td>0.242326</td>\n",
       "      <td>1.523836</td>\n",
       "      <td>1.444994</td>\n",
       "      <td>1.590111</td>\n",
       "      <td>1.512251</td>\n",
       "      <td>1.176150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4027</th>\n",
       "      <td>4027</td>\n",
       "      <td>2022-03-31 00:00:00+00:00</td>\n",
       "      <td>CEILAP-RG</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-51.600503</td>\n",
       "      <td>-69.319250</td>\n",
       "      <td>19.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.020506</td>\n",
       "      <td>0.022174</td>\n",
       "      <td>0.026260</td>\n",
       "      <td>0.030746</td>\n",
       "      <td>0.709865</td>\n",
       "      <td>0.440183</td>\n",
       "      <td>0.908389</td>\n",
       "      <td>0.575420</td>\n",
       "      <td>-0.820154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                   datetime       Name Andes?   season  \\\n",
       "4023        4023  2022-01-31 00:00:00+00:00    SP-EACH      N  dry_wet   \n",
       "4024        4024  2022-02-28 00:00:00+00:00  CEILAP-RG      N  dry_wet   \n",
       "4025        4025  2022-02-28 00:00:00+00:00    SP-EACH      N  dry_wet   \n",
       "4026        4026  2022-02-28 00:00:00+00:00  Sao_Paulo      N  dry_wet   \n",
       "4027        4027  2022-03-31 00:00:00+00:00  CEILAP-RG      N  dry_wet   \n",
       "\n",
       "       Latitude  Longitude  Elevation(m)  instrument_ID  AOD_870nm  AOD_675nm  \\\n",
       "4023 -23.481630 -46.499670         754.0          828.0   0.111798   0.148398   \n",
       "4024 -51.600503 -69.319250          19.0          737.0   0.019888   0.020804   \n",
       "4025 -23.481630 -46.499670         754.0          828.0   0.080296   0.114358   \n",
       "4026 -23.561500 -46.734983         786.0          669.0   0.085947   0.124275   \n",
       "4027 -51.600503 -69.319250          19.0          737.0   0.020506   0.022174   \n",
       "\n",
       "      AOD_500nm  AOD_440nm  440-870_Angstrom_Exponent  \\\n",
       "4023   0.209221   0.235761                   1.283426   \n",
       "4024   0.025685   0.031816                   0.812853   \n",
       "4025   0.183114   0.217191                   1.445593   \n",
       "4026   0.202389   0.242326                   1.523836   \n",
       "4027   0.026260   0.030746                   0.709865   \n",
       "\n",
       "      380-500_Angstrom_Exponent  440-675_Angstrom_Exponent  \\\n",
       "4023                   1.310844                   1.368162   \n",
       "4024                   0.790364                   1.203563   \n",
       "4025                   1.408980                   1.510809   \n",
       "4026                   1.444994                   1.590111   \n",
       "4027                   0.440183                   0.908389   \n",
       "\n",
       "      500-870_Angstrom_Exponent  340-440_Angstrom_Exponent  \n",
       "4023                   1.284635                   1.078179  \n",
       "4024                   0.552298                  -0.683922  \n",
       "4025                   1.449293                   1.140956  \n",
       "4026                   1.512251                   1.176150  \n",
       "4027                   0.575420                  -0.820154  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_ARNET_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.078915</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.081334</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.219307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime season            name   latitude  longitude  AER_elev  \\\n",
       "0 2014-01-15    NaN  ARM_Manacapuru  -3.212972 -60.598056     49.99   \n",
       "1 2014-01-15    NaN   Alta_Floresta  -9.871339 -56.104453    277.00   \n",
       "2 2014-01-15    NaN           Arica -18.471667 -70.313333     25.00   \n",
       "3 2014-01-15    NaN          CASLEO -31.798815 -69.295685   2485.00   \n",
       "4 2014-01-15    NaN       CEILAP-BA -34.555425 -58.506411     26.00   \n",
       "\n",
       "   AOD500_AERONET  AOD500_GC  \n",
       "0        0.078915        NaN  \n",
       "1        0.081334        NaN  \n",
       "2        0.219307        NaN  \n",
       "3        0.021907        NaN  \n",
       "4        0.107979        NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a seasonal dataframe to include both GC and AERONET seasonal averages\n",
    "seas_GC_AERNET_df= pd.DataFrame(index=ARNET_conc_df.index , columns =['datetime', 'season',\"name\", 'latitude', 'longitude', 'AER_elev',\n",
    "                                          'AOD500_AERONET','AOD500_GC'])\n",
    "seas_GC_AERNET_df['datetime']=ARNET_conc_df['datetime']\n",
    "seas_GC_AERNET_df['season']=seas_GC_AERNET_df['season']\n",
    "seas_GC_AERNET_df['name']=ARNET_conc_df['Name']\n",
    "seas_GC_AERNET_df['latitude']=ARNET_conc_df['Latitude']\n",
    "seas_GC_AERNET_df['longitude']=ARNET_conc_df['Longitude']\n",
    "seas_GC_AERNET_df['AER_elev']=ARNET_conc_df['Elevation(m)']\n",
    "seas_GC_AERNET_df['AOD500_AERONET']=ARNET_conc_df['AOD_500nm']\n",
    "seas_GC_AERNET_df['AOD500_GC']=np.nan\n",
    "seas_GC_AERNET_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
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       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
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       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
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       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>0.275963</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Temuco-UFRO_CEFOP</td>\n",
       "      <td>-38.745803</td>\n",
       "      <td>-72.615525</td>\n",
       "      <td>119.00</td>\n",
       "      <td>0.151302</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trelew</td>\n",
       "      <td>-43.249806</td>\n",
       "      <td>-65.308611</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.067009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNC-Palmira</td>\n",
       "      <td>3.512295</td>\n",
       "      <td>-76.307610</td>\n",
       "      <td>1065.20</td>\n",
       "      <td>0.222240</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UPC-GEAB-Valledupar</td>\n",
       "      <td>9.559600</td>\n",
       "      <td>-73.329300</td>\n",
       "      <td>157.00</td>\n",
       "      <td>0.157478</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime season                 name   latitude  longitude  AER_elev  \\\n",
       "0   2014-01-15    NaN       ARM_Manacapuru  -3.212972 -60.598056     49.99   \n",
       "1   2014-01-15    NaN        Alta_Floresta  -9.871339 -56.104453    277.00   \n",
       "2   2014-01-15    NaN                Arica -18.471667 -70.313333     25.00   \n",
       "3   2014-01-15    NaN               CASLEO -31.798815 -69.295685   2485.00   \n",
       "4   2014-01-15    NaN            CEILAP-BA -34.555425 -58.506411     26.00   \n",
       "..         ...    ...                  ...        ...        ...       ...   \n",
       "300 2019-07-15    NaN            Sao_Paulo -23.561500 -46.734983    786.00   \n",
       "301 2019-07-15    NaN    Temuco-UFRO_CEFOP -38.745803 -72.615525    119.00   \n",
       "302 2019-07-15    NaN               Trelew -43.249806 -65.308611     15.00   \n",
       "303 2019-07-15    NaN          UNC-Palmira   3.512295 -76.307610   1065.20   \n",
       "304 2019-07-15    NaN  UPC-GEAB-Valledupar   9.559600 -73.329300    157.00   \n",
       "\n",
       "     AOD500_AERONET  AOD500_GC  \n",
       "0          0.078915        NaN  \n",
       "1          0.081334        NaN  \n",
       "2          0.219307        NaN  \n",
       "3          0.021907        NaN  \n",
       "4          0.107979        NaN  \n",
       "..              ...        ...  \n",
       "300        0.275963        NaN  \n",
       "301        0.151302        NaN  \n",
       "302        0.067009        NaN  \n",
       "303        0.222240        NaN  \n",
       "304        0.157478        NaN  \n",
       "\n",
       "[305 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seas_GC_AERNET_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ARM_Manacapuru', 'Alta_Floresta', 'Arica', 'CASLEO', 'CEILAP-BA',\n",
       "       'CEILAP-Bariloche', 'CEILAP-Comodoro', 'CEILAP-Neuquen',\n",
       "       'CEILAP-RG', 'CUIABA-MIRANDA', 'Itajuba', 'Ji_Parana_SE', 'La_Paz',\n",
       "       'Manaus_EMBRAPA', 'Medellin', 'Petrolina_SONDA', 'Rio_Branco',\n",
       "       'Sao_Martinho_SONDA', 'Sao_Paulo', 'Trelew', 'Santiago_Beauchef',\n",
       "       'UdeConcepcion-CEFOP', 'Huancayo-IGP', 'UNC-Bogota', 'UNC-Gaitan',\n",
       "       'Brasilia_SONDA', 'Campo_Grande_SONDA', 'Paracou',\n",
       "       'SANTA_CRUZ_UTEPSA', 'Amazon_ATTO_Tower', 'Natal', 'Nouragues',\n",
       "       'SP-EACH', 'UPC-GEAB-Valledupar', 'Los_Leones_Auger',\n",
       "       'Pilar_Cordoba', 'Temuco-UFRO_CEFOP', 'UNC-Palmira',\n",
       "       'El_Alto-Altiplano', 'Ji_Parana', 'Paranal_CTA', 'Quito_USFQ',\n",
       "       'Mount_Chacaltaya', 'Tucuman', 'ARM_Cordoba', 'PSDA_Chile',\n",
       "       'SDU2018', 'Campus_USACH', 'Punta_Arenas_UMAG', 'Ibirapuera',\n",
       "       'Montevideo_FING'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seas_GC_AERNET_df['name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.167498</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.072333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.164211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.033346</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.095135</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime season            name   latitude  longitude  \\\n",
       "0 2014-01-31 00:00:00+00:00    NaN  ARM_Manacapuru  -3.212972 -60.598056   \n",
       "1 2014-01-31 00:00:00+00:00    NaN   Alta_Floresta  -9.871339 -56.104453   \n",
       "2 2014-01-31 00:00:00+00:00    NaN           Arica -18.471667 -70.313333   \n",
       "3 2014-01-31 00:00:00+00:00    NaN          CASLEO -31.798815 -69.295685   \n",
       "4 2014-01-31 00:00:00+00:00    NaN       CEILAP-BA -34.555425 -58.506411   \n",
       "\n",
       "   AER_elev  AOD500_AERONET  AOD500_GC  \n",
       "0     49.99        0.167498        NaN  \n",
       "1    277.00        0.072333        NaN  \n",
       "2     25.00        0.164211        NaN  \n",
       "3   2485.00        0.033346        NaN  \n",
       "4     26.00        0.095135        NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a monthly  dataframe to include both GC and AERONET seasonal averages\n",
    "#making sure that monthly AERONET df is in datetime\n",
    "monthly_ARNET_df['datetime']=pd.to_datetime(monthly_ARNET_df['datetime'])\n",
    "#selecting lonly the years that are needed\n",
    "monthly_ARNET_subdf=monthly_ARNET_df[(monthly_ARNET_df['datetime'] >= '2014-01-01') & (monthly_ARNET_df['datetime'] < '2019-01-01')]\n",
    "monthly_ARNET_subdf=monthly_ARNET_subdf.reset_index()\n",
    "mon_GC_AERNET_df= pd.DataFrame(index=monthly_ARNET_subdf.index , columns =['datetime', 'season',\"name\", 'latitude', 'longitude', 'AER_elev',\n",
    "                                          'AOD500_AERONET','AOD500_GC'])\n",
    "mon_GC_AERNET_df['datetime']=monthly_ARNET_subdf['datetime']\n",
    "mon_GC_AERNET_df['season']=mon_GC_AERNET_df['season']\n",
    "mon_GC_AERNET_df['name']=monthly_ARNET_subdf['Name']\n",
    "mon_GC_AERNET_df['latitude']=monthly_ARNET_subdf['Latitude']\n",
    "mon_GC_AERNET_df['longitude']=monthly_ARNET_subdf['Longitude']\n",
    "mon_GC_AERNET_df['AER_elev']=monthly_ARNET_subdf['Elevation(m)']\n",
    "mon_GC_AERNET_df['AOD500_AERONET']=monthly_ARNET_subdf['AOD_500nm']\n",
    "mon_GC_AERNET_df['AOD500_GC']=np.nan\n",
    "mon_GC_AERNET_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Name</th>\n",
       "      <th>Andes?</th>\n",
       "      <th>season</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation(m)</th>\n",
       "      <th>instrument_ID</th>\n",
       "      <th>AOD_870nm</th>\n",
       "      <th>AOD_675nm</th>\n",
       "      <th>AOD_500nm</th>\n",
       "      <th>AOD_440nm</th>\n",
       "      <th>440-870_Angstrom_Exponent</th>\n",
       "      <th>380-500_Angstrom_Exponent</th>\n",
       "      <th>440-675_Angstrom_Exponent</th>\n",
       "      <th>500-870_Angstrom_Exponent</th>\n",
       "      <th>340-440_Angstrom_Exponent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2106</td>\n",
       "      <td>2106</td>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0.098619</td>\n",
       "      <td>0.119181</td>\n",
       "      <td>0.167498</td>\n",
       "      <td>0.186125</td>\n",
       "      <td>0.953499</td>\n",
       "      <td>1.013626</td>\n",
       "      <td>1.081957</td>\n",
       "      <td>0.962132</td>\n",
       "      <td>0.971345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2107</td>\n",
       "      <td>2107</td>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.047628</td>\n",
       "      <td>0.051626</td>\n",
       "      <td>0.072333</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.852222</td>\n",
       "      <td>1.490504</td>\n",
       "      <td>1.116723</td>\n",
       "      <td>0.808722</td>\n",
       "      <td>1.358528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2108</td>\n",
       "      <td>2108</td>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Arica</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.118220</td>\n",
       "      <td>0.122646</td>\n",
       "      <td>0.164211</td>\n",
       "      <td>0.182261</td>\n",
       "      <td>0.659022</td>\n",
       "      <td>1.049872</td>\n",
       "      <td>0.916812</td>\n",
       "      <td>0.594135</td>\n",
       "      <td>1.043982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2109</td>\n",
       "      <td>2109</td>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>Y</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.024964</td>\n",
       "      <td>0.025795</td>\n",
       "      <td>0.033346</td>\n",
       "      <td>0.040709</td>\n",
       "      <td>0.797387</td>\n",
       "      <td>1.650597</td>\n",
       "      <td>1.300332</td>\n",
       "      <td>0.477098</td>\n",
       "      <td>0.730037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2110</td>\n",
       "      <td>2110</td>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>N</td>\n",
       "      <td>dry_wet</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.058549</td>\n",
       "      <td>0.067533</td>\n",
       "      <td>0.095135</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>0.899632</td>\n",
       "      <td>1.340977</td>\n",
       "      <td>1.096048</td>\n",
       "      <td>0.829249</td>\n",
       "      <td>0.766588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Unnamed: 0                  datetime            Name Andes?  \\\n",
       "0   2106        2106 2014-01-31 00:00:00+00:00  ARM_Manacapuru      N   \n",
       "1   2107        2107 2014-01-31 00:00:00+00:00   Alta_Floresta      N   \n",
       "2   2108        2108 2014-01-31 00:00:00+00:00           Arica      N   \n",
       "3   2109        2109 2014-01-31 00:00:00+00:00          CASLEO      Y   \n",
       "4   2110        2110 2014-01-31 00:00:00+00:00       CEILAP-BA      N   \n",
       "\n",
       "    season   Latitude  Longitude  Elevation(m)  instrument_ID  AOD_870nm  \\\n",
       "0  dry_wet  -3.212972 -60.598056         49.99          620.0   0.098619   \n",
       "1  dry_wet  -9.871339 -56.104453        277.00          635.0   0.047628   \n",
       "2  dry_wet -18.471667 -70.313333         25.00          528.0   0.118220   \n",
       "3  dry_wet -31.798815 -69.295685       2485.00          781.0   0.024964   \n",
       "4  dry_wet -34.555425 -58.506411         26.00          800.0   0.058549   \n",
       "\n",
       "   AOD_675nm  AOD_500nm  AOD_440nm  440-870_Angstrom_Exponent  \\\n",
       "0   0.119181   0.167498   0.186125                   0.953499   \n",
       "1   0.051626   0.072333   0.080357                   0.852222   \n",
       "2   0.122646   0.164211   0.182261                   0.659022   \n",
       "3   0.025795   0.033346   0.040709                   0.797387   \n",
       "4   0.067533   0.095135   0.111455                   0.899632   \n",
       "\n",
       "   380-500_Angstrom_Exponent  440-675_Angstrom_Exponent  \\\n",
       "0                   1.013626                   1.081957   \n",
       "1                   1.490504                   1.116723   \n",
       "2                   1.049872                   0.916812   \n",
       "3                   1.650597                   1.300332   \n",
       "4                   1.340977                   1.096048   \n",
       "\n",
       "   500-870_Angstrom_Exponent  340-440_Angstrom_Exponent  \n",
       "0                   0.962132                   0.971345  \n",
       "1                   0.808722                   1.358528  \n",
       "2                   0.594135                   1.043982  \n",
       "3                   0.477098                   0.730037  \n",
       "4                   0.829249                   0.766588  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_ARNET_subdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1462663106.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['season'][site]=label_s\n",
      "/tmp/ipykernel_22513/1462663106.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['season'][site]=label_s\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.078915</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.081334</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.219307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>0.275963</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Temuco-UFRO_CEFOP</td>\n",
       "      <td>-38.745803</td>\n",
       "      <td>-72.615525</td>\n",
       "      <td>119.00</td>\n",
       "      <td>0.151302</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Trelew</td>\n",
       "      <td>-43.249806</td>\n",
       "      <td>-65.308611</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.067009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>UNC-Palmira</td>\n",
       "      <td>3.512295</td>\n",
       "      <td>-76.307610</td>\n",
       "      <td>1065.20</td>\n",
       "      <td>0.222240</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>UPC-GEAB-Valledupar</td>\n",
       "      <td>9.559600</td>\n",
       "      <td>-73.329300</td>\n",
       "      <td>157.00</td>\n",
       "      <td>0.157478</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime season                 name   latitude  longitude  AER_elev  \\\n",
       "0   2014-01-15    Wet       ARM_Manacapuru  -3.212972 -60.598056     49.99   \n",
       "1   2014-01-15    Wet        Alta_Floresta  -9.871339 -56.104453    277.00   \n",
       "2   2014-01-15    Wet                Arica -18.471667 -70.313333     25.00   \n",
       "3   2014-01-15    Wet               CASLEO -31.798815 -69.295685   2485.00   \n",
       "4   2014-01-15    Wet            CEILAP-BA -34.555425 -58.506411     26.00   \n",
       "..         ...    ...                  ...        ...        ...       ...   \n",
       "300 2019-07-15    Dry            Sao_Paulo -23.561500 -46.734983    786.00   \n",
       "301 2019-07-15    Dry    Temuco-UFRO_CEFOP -38.745803 -72.615525    119.00   \n",
       "302 2019-07-15    Dry               Trelew -43.249806 -65.308611     15.00   \n",
       "303 2019-07-15    Dry          UNC-Palmira   3.512295 -76.307610   1065.20   \n",
       "304 2019-07-15    Dry  UPC-GEAB-Valledupar   9.559600 -73.329300    157.00   \n",
       "\n",
       "     AOD500_AERONET  AOD500_GC  \n",
       "0          0.078915        NaN  \n",
       "1          0.081334        NaN  \n",
       "2          0.219307        NaN  \n",
       "3          0.021907        NaN  \n",
       "4          0.107979        NaN  \n",
       "..              ...        ...  \n",
       "300        0.275963        NaN  \n",
       "301        0.151302        NaN  \n",
       "302        0.067009        NaN  \n",
       "303        0.222240        NaN  \n",
       "304        0.157478        NaN  \n",
       "\n",
       "[305 rows x 8 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##labeling the seasons with \"Wet or \"Dry\" for seasonal GC_AERNET dataframe\n",
    "for site in range(0,len(ARNET_conc_df['Name'])): \n",
    "    time= ARNET_conc_df['datetime'][site]\n",
    "    month = time.month\n",
    "    year = time.year\n",
    "    if (month == 1):\n",
    "        label_s='Wet'\n",
    "        seas_GC_AERNET_df['season'][site]=label_s\n",
    "    elif (month == 7):\n",
    "        label_s='Dry'\n",
    "        seas_GC_AERNET_df['season'][site]=label_s\n",
    "seas_GC_AERNET_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/2933909314.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['season'][site]=label_s\n",
      "/tmp/ipykernel_22513/2933909314.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['season'][site]=label_s\n",
      "/tmp/ipykernel_22513/2933909314.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['season'][site]=label_s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.167498</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.072333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.164211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.033346</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.095135</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime season            name   latitude  longitude  \\\n",
       "0 2014-01-31 00:00:00+00:00    Wet  ARM_Manacapuru  -3.212972 -60.598056   \n",
       "1 2014-01-31 00:00:00+00:00    Wet   Alta_Floresta  -9.871339 -56.104453   \n",
       "2 2014-01-31 00:00:00+00:00    Wet           Arica -18.471667 -70.313333   \n",
       "3 2014-01-31 00:00:00+00:00    Wet          CASLEO -31.798815 -69.295685   \n",
       "4 2014-01-31 00:00:00+00:00    Wet       CEILAP-BA -34.555425 -58.506411   \n",
       "\n",
       "   AER_elev  AOD500_AERONET  AOD500_GC  \n",
       "0     49.99        0.167498        NaN  \n",
       "1    277.00        0.072333        NaN  \n",
       "2     25.00        0.164211        NaN  \n",
       "3   2485.00        0.033346        NaN  \n",
       "4     26.00        0.095135        NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##labeling the seasons with \"Wet or \"Dry\" for seasonal GC_AERNET dataframe\n",
    "for site in range(0,len(monthly_ARNET_subdf['Name'])): \n",
    "    time= monthly_ARNET_subdf['datetime'][site]\n",
    "    month = time.month\n",
    "    year = time.year\n",
    "    if (month == 1 or month ==2 or month ==3 or month ==4 or month ==5):\n",
    "        label_s='Wet'\n",
    "        mon_GC_AERNET_df['season'][site]=label_s\n",
    "    elif (month == 7 or month ==8 or month ==9 or month ==10 or month ==11):\n",
    "        label_s='Dry'\n",
    "        mon_GC_AERNET_df['season'][site]=label_s\n",
    "    else:\n",
    "        label_s='Trans'\n",
    "        mon_GC_AERNET_df['season'][site]=label_s\n",
    "mon_GC_AERNET_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/688503747.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.078915</td>\n",
       "      <td>0.048310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.081334</td>\n",
       "      <td>0.044638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.219307</td>\n",
       "      <td>0.122834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>0.024546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>0.105442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>0.275963</td>\n",
       "      <td>0.295570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Temuco-UFRO_CEFOP</td>\n",
       "      <td>-38.745803</td>\n",
       "      <td>-72.615525</td>\n",
       "      <td>119.00</td>\n",
       "      <td>0.151302</td>\n",
       "      <td>0.059614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Trelew</td>\n",
       "      <td>-43.249806</td>\n",
       "      <td>-65.308611</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.067009</td>\n",
       "      <td>0.040917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>UNC-Palmira</td>\n",
       "      <td>3.512295</td>\n",
       "      <td>-76.307610</td>\n",
       "      <td>1065.20</td>\n",
       "      <td>0.222240</td>\n",
       "      <td>0.059301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2019-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>UPC-GEAB-Valledupar</td>\n",
       "      <td>9.559600</td>\n",
       "      <td>-73.329300</td>\n",
       "      <td>157.00</td>\n",
       "      <td>0.157478</td>\n",
       "      <td>0.101916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime season                 name   latitude  longitude  AER_elev  \\\n",
       "0   2014-01-15    Wet       ARM_Manacapuru  -3.212972 -60.598056     49.99   \n",
       "1   2014-01-15    Wet        Alta_Floresta  -9.871339 -56.104453    277.00   \n",
       "2   2014-01-15    Wet                Arica -18.471667 -70.313333     25.00   \n",
       "3   2014-01-15    Wet               CASLEO -31.798815 -69.295685   2485.00   \n",
       "4   2014-01-15    Wet            CEILAP-BA -34.555425 -58.506411     26.00   \n",
       "..         ...    ...                  ...        ...        ...       ...   \n",
       "300 2019-07-15    Dry            Sao_Paulo -23.561500 -46.734983    786.00   \n",
       "301 2019-07-15    Dry    Temuco-UFRO_CEFOP -38.745803 -72.615525    119.00   \n",
       "302 2019-07-15    Dry               Trelew -43.249806 -65.308611     15.00   \n",
       "303 2019-07-15    Dry          UNC-Palmira   3.512295 -76.307610   1065.20   \n",
       "304 2019-07-15    Dry  UPC-GEAB-Valledupar   9.559600 -73.329300    157.00   \n",
       "\n",
       "     AOD500_AERONET  AOD500_GC  \n",
       "0          0.078915   0.048310  \n",
       "1          0.081334   0.044638  \n",
       "2          0.219307   0.122834  \n",
       "3          0.021907   0.024546  \n",
       "4          0.107979   0.105442  \n",
       "..              ...        ...  \n",
       "300        0.275963   0.295570  \n",
       "301        0.151302   0.059614  \n",
       "302        0.067009   0.040917  \n",
       "303        0.222240   0.059301  \n",
       "304        0.157478   0.101916  \n",
       "\n",
       "[305 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loops through the GEOS-Chem AOD xr.DataSet\n",
    "for period in range(0,len(AOD_2014_2019_dt['time'].values)):\n",
    "    #pick the date for the correspoding dataset\n",
    "    time= AOD_2014_2019_dt['time'][period]\n",
    "    month = time.dt.month.values\n",
    "    year = time.dt.year.values\n",
    "    #subsets the AERONET dataframe to the correct time period\n",
    "    subset_df_ARNET=ARNET_conc_df[ARNET_conc_df['datetime']==str(year) +'-0'+str(month)+'-15']\n",
    "    #for each site in the subset, it looks for the corresponding GC-AOD value\n",
    "    for site in range(0,len(subset_df_ARNET['Name'])):\n",
    "        lat_AER,lon_AER, idx = subset_df_ARNET['Latitude'].values[site],subset_df_ARNET['Longitude'].values[site], subset_df_ARNET.index[site]\n",
    "        #print(lat_AER)\n",
    "        subset_GC=AOD_2014_2019_dt[\"AOD500nm_total\"][period].sel(lat= lat_AER,\n",
    "                                                    lon=lon_AER, method='nearest')\n",
    "        #add the value for GC to the season dataframe\n",
    "        seas_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
    "seas_GC_AERNET_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/n/home10/ebonilla/.conda/envs/pie_research/lib/python3.8/site-packages/xarray/core/indexes.py:234: FutureWarning: Passing method to Float64Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.\n",
      "  indexer = self.index.get_loc(\n",
      "/tmp/ipykernel_22513/1390121329.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>ARM_Manacapuru</td>\n",
       "      <td>-3.212972</td>\n",
       "      <td>-60.598056</td>\n",
       "      <td>49.99</td>\n",
       "      <td>0.167498</td>\n",
       "      <td>0.062539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.00</td>\n",
       "      <td>0.072333</td>\n",
       "      <td>0.045940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.164211</td>\n",
       "      <td>0.131637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>0.033346</td>\n",
       "      <td>0.054455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-31 00:00:00+00:00</td>\n",
       "      <td>Wet</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0.095135</td>\n",
       "      <td>0.107437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>2018-12-31 00:00:00+00:00</td>\n",
       "      <td>Trans</td>\n",
       "      <td>SP-EACH</td>\n",
       "      <td>-23.481630</td>\n",
       "      <td>-46.499670</td>\n",
       "      <td>754.00</td>\n",
       "      <td>0.182726</td>\n",
       "      <td>0.213884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>2018-12-31 00:00:00+00:00</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Sao_Paulo</td>\n",
       "      <td>-23.561500</td>\n",
       "      <td>-46.734983</td>\n",
       "      <td>786.00</td>\n",
       "      <td>0.181661</td>\n",
       "      <td>0.259964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>2018-12-31 00:00:00+00:00</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Tucuman</td>\n",
       "      <td>-26.787153</td>\n",
       "      <td>-65.206697</td>\n",
       "      <td>481.00</td>\n",
       "      <td>0.096835</td>\n",
       "      <td>0.072980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>2018-12-31 00:00:00+00:00</td>\n",
       "      <td>Trans</td>\n",
       "      <td>UNC-Palmira</td>\n",
       "      <td>3.512295</td>\n",
       "      <td>-76.307610</td>\n",
       "      <td>1065.20</td>\n",
       "      <td>0.272486</td>\n",
       "      <td>0.052413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>2018-12-31 00:00:00+00:00</td>\n",
       "      <td>Trans</td>\n",
       "      <td>UPC-GEAB-Valledupar</td>\n",
       "      <td>9.559600</td>\n",
       "      <td>-73.329300</td>\n",
       "      <td>157.00</td>\n",
       "      <td>0.151459</td>\n",
       "      <td>0.075259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1330 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      datetime season                 name   latitude  \\\n",
       "0    2014-01-31 00:00:00+00:00    Wet       ARM_Manacapuru  -3.212972   \n",
       "1    2014-01-31 00:00:00+00:00    Wet        Alta_Floresta  -9.871339   \n",
       "2    2014-01-31 00:00:00+00:00    Wet                Arica -18.471667   \n",
       "3    2014-01-31 00:00:00+00:00    Wet               CASLEO -31.798815   \n",
       "4    2014-01-31 00:00:00+00:00    Wet            CEILAP-BA -34.555425   \n",
       "...                        ...    ...                  ...        ...   \n",
       "1325 2018-12-31 00:00:00+00:00  Trans              SP-EACH -23.481630   \n",
       "1326 2018-12-31 00:00:00+00:00  Trans            Sao_Paulo -23.561500   \n",
       "1327 2018-12-31 00:00:00+00:00  Trans              Tucuman -26.787153   \n",
       "1328 2018-12-31 00:00:00+00:00  Trans          UNC-Palmira   3.512295   \n",
       "1329 2018-12-31 00:00:00+00:00  Trans  UPC-GEAB-Valledupar   9.559600   \n",
       "\n",
       "      longitude  AER_elev  AOD500_AERONET  AOD500_GC  \n",
       "0    -60.598056     49.99        0.167498   0.062539  \n",
       "1    -56.104453    277.00        0.072333   0.045940  \n",
       "2    -70.313333     25.00        0.164211   0.131637  \n",
       "3    -69.295685   2485.00        0.033346   0.054455  \n",
       "4    -58.506411     26.00        0.095135   0.107437  \n",
       "...         ...       ...             ...        ...  \n",
       "1325 -46.499670    754.00        0.182726   0.213884  \n",
       "1326 -46.734983    786.00        0.181661   0.259964  \n",
       "1327 -65.206697    481.00        0.096835   0.072980  \n",
       "1328 -76.307610   1065.20        0.272486   0.052413  \n",
       "1329 -73.329300    157.00        0.151459   0.075259  \n",
       "\n",
       "[1330 rows x 8 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loops through the monhtly GEOS-Chem AOD xr.DataSet\n",
    "for period in range(0,len(AOD_2014_2019_mon['time'].values)):\n",
    "    #pick the date for the correspoding dataset\n",
    "    time= AOD_2014_2019_mon['time'][period]\n",
    "    month = time.dt.month.values\n",
    "    year = time.dt.year.values\n",
    "    #print(month)\n",
    "    #subsets the AERONET dataframe to the correct time period\n",
    "    subset_df_ARNET=monthly_ARNET_subdf[(monthly_ARNET_subdf['datetime'].dt.year==year) & (monthly_ARNET_subdf['datetime'].dt.month==month)]\n",
    "    #print(subset_df_ARNET['Latitude'])\n",
    "    #for each site in the subset, it looks for the corresponding GC-AOD value\n",
    "    for site in range(0,len(subset_df_ARNET['Name'])):\n",
    "        lat_AER,lon_AER, idx = subset_df_ARNET['Latitude'].values[site],subset_df_ARNET['Longitude'].values[site], subset_df_ARNET.index[site]\n",
    "        #print(lat_AER)\n",
    "        subset_GC=AOD_2014_2019_mon[\"AOD500nm_total\"][period].sel(lat= lat_AER,\n",
    "                                                    lon=lon_AER, method='nearest')\n",
    "        #add the value for GC to the season dataframe\n",
    "        mon_GC_AERNET_df['AOD500_GC'][idx]=subset_GC.values\n",
    "mon_GC_AERNET_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7112803136005105"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_AOD_GC = seas_GC_AERNET_df[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "corr_AOD_GC_mon = mon_GC_AERNET_df[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "corr_AOD_GC_mon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dry_GC_AERNET_df=seas_GC_AERNET_df[seas_GC_AERNET_df['season']==\"Dry\"]\n",
    "wet_GC_AERNET_df=seas_GC_AERNET_df[seas_GC_AERNET_df['season']==\"Wet\"]\n",
    "\n",
    "dry_GC_AERNET_mon=mon_GC_AERNET_df[mon_GC_AERNET_df['season']==\"Dry\"]\n",
    "wet_GC_AERNET_mon=mon_GC_AERNET_df[mon_GC_AERNET_df['season']==\"Wet\"]\n",
    "trans_GC_AERNET_mon=mon_GC_AERNET_df[mon_GC_AERNET_df['season']==\"Trans\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_AOD_GC_dry = dry_GC_AERNET_df[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "corr_AOD_GC_wet = wet_GC_AERNET_df[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "\n",
    "corr_AOD_GC_dry_mon = dry_GC_AERNET_mon[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "corr_AOD_GC_wet_mon = wet_GC_AERNET_mon[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]\n",
    "corr_AOD_GC_trans_mon = trans_GC_AERNET_mon[['AOD500_AERONET','AOD500_GC']].corr().iloc[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "fig, axes = plt.subplots(1, figsize=[14, 8]) #8,8\n",
    "\n",
    "colors = {'Wet':'C0', 'Dry':'C3', 'Trans':\"gray\"}\n",
    "\n",
    "grouped = mon_GC_AERNET_df.groupby('season')\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=axes, kind='scatter', x='AOD500_AERONET', y='AOD500_GC', label=key, color=colors[key]\n",
    "              ,s=200, alpha=0.75)\n",
    "axes.plot([],[],' ',label = \"Correlation: \" + '%s' % float('%.3f' %corr_AOD_GC_mon))\n",
    "axes.plot([],[],' ',label = \"Corr. Dry: \" + '%s' % float('%.3f' %corr_AOD_GC_dry_mon))\n",
    "axes.plot([],[],' ',label = \"Corr. Trans: \" + '%s' % float('%.3f' %corr_AOD_GC_trans_mon))\n",
    "axes.plot([],[],' ',label = \"Corr. Wet: \" + '%s' % float('%.3f' %corr_AOD_GC_wet_mon))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "axes.legend(loc='upper center', bbox_to_anchor=(1.25, 0.75),\n",
    "          fancybox=False, shadow=False, ncol=1)\n",
    "\n",
    "line = mlines.Line2D([0, 1], [0, 1], color='gray', linestyle='dashed',linewidth=4 )\n",
    "transform = axes.transAxes\n",
    "line.set_transform(transform)\n",
    "axes.add_line(line)\n",
    "\n",
    "ticks = np.arange(0, 0.9, 0.1)\n",
    "#axes.legend()\n",
    "axes.set_xlim([0,0.82])\n",
    "axes.set_ylim([0,0.82])\n",
    "axes.set_yticks(ticks)\n",
    "axes.set_xticks(ticks)\n",
    "axes.set_xlabel('AERONET AOD at 500 nm')\n",
    "axes.set_ylabel('GEOS-Chem AOD at 500 nm')\n",
    "axes.set_title('Monthly aerosol optical depths in South America', pad=20, fontsize=24);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure S2---used in paper\n",
    "# seasonal averages\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.transforms as mtransforms\n",
    "\n",
    "fig, axes = plt.subplots(1, figsize=[8, 8],dpi=300)\n",
    "\n",
    "colors = {'Wet':'C0', 'Dry':'C3'}\n",
    "\n",
    "grouped = seas_GC_AERNET_df.groupby('season')\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=axes, kind='scatter', x='AOD500_AERONET', y='AOD500_GC', label=key, color=colors[key]\n",
    "              ,s=200, alpha=0.75)\n",
    "axes.plot([],[],' ',label = \"Correlation: \" + '%s' % float('%.2f' %corr_AOD_GC))\n",
    "axes.plot([],[],' ',label = \"Dry season correlation: \" + '%s' % float('%.3f' %corr_AOD_GC_dry))\n",
    "axes.plot([],[],' ',label = \"Wet season correlation: \" + '%s' % float('%.2f' %corr_AOD_GC_wet))\n",
    "#plt.show()\n",
    "\n",
    "#axes.scatter(seas_GC_AERNET_df['AOD500_AERONET'], seas_GC_AERNET_df[\"AOD500_GC\"], \n",
    "#             c=seas_GC_AERNET_df['season'].map(colors),s=200, label=label_s, alpha=0.75)\n",
    "\n",
    "axes.legend(loc='upper center', bbox_to_anchor=(1.35, 0.75),\n",
    "          fancybox=False, shadow=False, ncol=1)\n",
    "\n",
    "line = mlines.Line2D([0, 1], [0, 1], color='gray', linestyle='dashed',linewidth=4 )\n",
    "transform = axes.transAxes\n",
    "line.set_transform(transform)\n",
    "axes.add_line(line)\n",
    "\n",
    "ticks = np.arange(0, 0.5, 0.1)\n",
    "#axes.legend()\n",
    "axes.set_xlim([0,0.42])\n",
    "axes.set_ylim([0,0.42])\n",
    "axes.set_yticks(ticks)\n",
    "axes.set_xticks(ticks)\n",
    "axes.set_xlabel('AERONET AOD at 500 nm')\n",
    "axes.set_ylabel('GEOS-Chem AOD at 500 nm')\n",
    "axes.set_title('Aerosol optical depths in South America', pad=20, fontsize=24);\n",
    "fig.savefig('../PP2_figs/AmazonHealth_FigureS2a.png', dpi=fig.dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>AER_elev</th>\n",
       "      <th>AOD500_AERONET</th>\n",
       "      <th>AOD500_GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Alta_Floresta</td>\n",
       "      <td>-9.871339</td>\n",
       "      <td>-56.104453</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.295369</td>\n",
       "      <td>0.199558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>Arica</td>\n",
       "      <td>-18.471667</td>\n",
       "      <td>-70.313333</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.206097</td>\n",
       "      <td>0.104785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>CASLEO</td>\n",
       "      <td>-31.798815</td>\n",
       "      <td>-69.295685</td>\n",
       "      <td>2485.0</td>\n",
       "      <td>0.015358</td>\n",
       "      <td>0.018067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>CEILAP-BA</td>\n",
       "      <td>-34.555425</td>\n",
       "      <td>-58.506411</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.096743</td>\n",
       "      <td>0.085832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>Dry</td>\n",
       "      <td>CEILAP-Bariloche</td>\n",
       "      <td>-41.146944</td>\n",
       "      <td>-71.163333</td>\n",
       "      <td>840.0</td>\n",
       "      <td>0.081707</td>\n",
       "      <td>0.030634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime season              name   latitude  longitude  AER_elev  \\\n",
       "20 2014-07-15    Dry     Alta_Floresta  -9.871339 -56.104453     277.0   \n",
       "21 2014-07-15    Dry             Arica -18.471667 -70.313333      25.0   \n",
       "22 2014-07-15    Dry            CASLEO -31.798815 -69.295685    2485.0   \n",
       "23 2014-07-15    Dry         CEILAP-BA -34.555425 -58.506411      26.0   \n",
       "24 2014-07-15    Dry  CEILAP-Bariloche -41.146944 -71.163333     840.0   \n",
       "\n",
       "    AOD500_AERONET  AOD500_GC  \n",
       "20        0.295369   0.199558  \n",
       "21        0.206097   0.104785  \n",
       "22        0.015358   0.018067  \n",
       "23        0.096743   0.085832  \n",
       "24        0.081707   0.030634  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_GC_AERNET_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized mean error/bias from wet season:-0.36165105474288467\n",
      "normalized mean error/bias from dry season:-0.34244950049551554\n"
     ]
    }
   ],
   "source": [
    "# Norma\n",
    "xmod_wet= wet_GC_AERNET_df['AOD500_GC']\n",
    "xobs_wet= wet_GC_AERNET_df['AOD500_AERONET']\n",
    "xdiff_wet= xmod_wet-xobs_wet\n",
    "NME_aod_wet=((np.sum((xdiff_wet)))/(np.sum(xobs_wet)))\n",
    "xmod_dry= dry_GC_AERNET_df['AOD500_GC']\n",
    "xobs_dry= dry_GC_AERNET_df['AOD500_AERONET']\n",
    "xdiff_dry= xmod_dry-xobs_dry\n",
    "NME_aod_dry=((np.sum((xdiff_dry)))/(np.sum((xobs_dry))))\n",
    "print('normalized mean error/bias from wet season:' + str(NME_aod_wet) +'\\nnormalized mean error/bias from dry season:'+ str(NME_aod_dry))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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